WO2009086143A2 - Mortgage fraud detection systems and methods - Google Patents
Mortgage fraud detection systems and methods Download PDFInfo
- Publication number
- WO2009086143A2 WO2009086143A2 PCT/US2008/087745 US2008087745W WO2009086143A2 WO 2009086143 A2 WO2009086143 A2 WO 2009086143A2 US 2008087745 W US2008087745 W US 2008087745W WO 2009086143 A2 WO2009086143 A2 WO 2009086143A2
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- data
- records
- information
- loan
- loan application
- Prior art date
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/02—Banking, e.g. interest calculation or account maintenance
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/018—Certifying business or products
- G06Q30/0185—Product, service or business identity fraud
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/03—Credit; Loans; Processing thereof
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/16—Real estate
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/18—Legal services; Handling legal documents
Definitions
- the various embodiments of the present invention relate generally to database and information management systems and, more particularly, to fraud detection database systems and methods to assist users to detect mortgage fraud as well as other types of financial fraud that may occur in a financial application process.
- Mortgage fraud typically occurs in two ways: fraud for profit and fraud for housing or property. Fraud for profit schemes typically result in ill-gotten gains from falsified or fraudulent loan transactions and usually involve industry insiders well versed in the funding process. The insiders that are typically parties to fraudulent schemes make it challenging to uncover fraudulent activities. Financial losses stemming from this type of fraudulent activity can be significant and devastating to industry participants. Examples of insider contributed fraud include, but are not limited to, illegal flipping, straw buyer scams, equity skimming, and fraudulent property investments. [0006] Fraud for housing or property generally involves factual misstatements to obtain a property as a primary residence. This type of fraud contributes to the greatest amount of reported fraud.
- Common misstatements include embellishing salary or income amounts and undisclosed borrowed funds or employment terms. Borrowers are often coached by insiders so that reported data is represented more favorably and appears less risky to lenders.
- common mortgage application fraud plots include equity skimming/foreclosure bailout schemes, churning, chunking, shot-gunning, silent second, property flipping, double escrow, straw-buying, air loans, identification theft, asset rentals, mortgage elimination schemes, cash- back schemes, and non-arm's length transactions.
- Shot-gunning An individual takes out undisclosed multiple loans on a single property simultaneously and then disappears with the proceeds. This scheme is often associated with foreign investors and organized crime.
- Silent Second A hidden second mortgage on a property. For example, a seller gives a borrower a second mortgage that is not disclosed to the borrow or to the lender of the first mortgage. To conceal its existence, the second mortgage is usually not recorded until after the closing. The seller receives the proceeds of the mortgage, and the borrower has to make the mortgage payments.
- flipping constitutes fraud when there is a significant value increase despite only minimal improvements to the property.
- a fraudulent appraisal is almost always involved.
- Illegal flipping can ruin the value of homes and neighborhoods.
- lenders can lose money by providing large loans on over- valued properties.
- Double Escrow Two closings on the same property at once. For example, a borrower buys a property at one price and immediately sells the property at a higher price. Double escrow is a property flip with a shortened time frame, and is illegal if all terms are not disclosed to the involved parties.
- Straw-buying Using someone else's credit to secure a loan. The person whose credit is being used is the "straw buyer.” The perpetrating parties are often related. For example, 838 l.DOC 1 if an individual's brother cannot secure a loan to buy a house, the individual can offer his own credentials to secure the loan for his brother. For another example, an individual with good credit may be approached and offered money to lend his name and good credit to multiple transactions.
- Air Loans All documentation is fabricated to secure a loan. There is no property and no borrower.
- ID Identification
- Theft Identity of a borrower or a mortgage professional is stolen and misused in a loan transaction.
- Asset Rentals Programs where assets, such as bank balances, cash, and strong credit card lines, are “borrowed” from their owners to make perspective borrowers appear financially sound. This scheme appears frequently on the internet. Usually, the asset "lenders” are compensated in some way.
- misrepresentations on a mortgage application inflated appraisals, appraisals with inappropriate comparables, employment and/or income misrepresentation (for example, on pay stubs, W-2s, tax returns, or Verifications of Employment), failure to disclose debts and/or other liabilities, failure to disclose correct employment status, use of invalid borrower SSNs, fabricated or misrepresented monthly housing payments (e.g., rent or mortgage payments), falsified bank statements or accounts, falsified gift letters, occupancy misrepresentation (e.g., primary residence vs. investment property), incorrect transaction type (e.g., purchase vs. refinance), borrower and/or seller not properly listed on
- various embodiments of the present invention generally comprise a fraud detection database, a fraud detection database system for detecting signs of fraud, and methods for analyzing various data resources to detect fraud.
- Fraud detection may occur in mortgage loan applications as well as many other financial applications (e.g., auto loan, credit card, and many other personal property loans).
- System users such as lenders, can submit loan application data to a fraud detection system.
- Loan application data can include information from a borrower's mortgage loan application, such as information entered on a Uniform Residential Loan Application (Form 1003).
- Other submitted information may also include additional information from lenders and originator information, information on brokers, realtors, or appraisers involved in the borrower's transaction.
- Detecting fraud through a database system can generally comprise providing a database for storing and/or maintaining a set of records.
- the records can include various types of financial information.
- a database operator can access, receive, and/or utilize financial information records from a plurality of sources including pre-existing databases and/or lending institutions.
- a database operator can configure a fraud detection system to compare a first data record to a second data record to determine whether the two records are preliminary matches.
- the first data record may be received from a lending institution and the second data
- DOC 5 record may include pre-existing stored data. Records can be preliminary matches if they have certain data in common. When records represent loan applications, for example, the data can include any one or more of borrower names, borrower SSNs, and real property addresses for which a mortgage loan is requested. [00013] Preliminary matches can also be compared for inconsistent information for fraud detection. If inconsistent information is found, the inconsistent preliminarily matching records may be flagged. The inconsistent information need not be the same category of information examined to determine preliminary matching. For example, two application data sets with the same borrower name or property address but with different appraisal values can be flagged in the database.
- Embodiments of the present invention also enable report generation so that end users can review results of data analysis.
- end users may be system subscribers that submitted one or more data records to a fraud detection system. Reports preferably show results of data comparisons in a format easily and readily understood by end users.
- fraud detection embodiments of the present invention are configured to receive input data records. Such data input can be provided by end users who are using embodiments of the present invention to detect fraud in a financial loan environment. As records are received from end users, received records can be compared with other records. Comparisons can be made initially upon receipt and also conducted at predetermined frequencies to continue monitoring for possible fraudulent activities. [00016] To implement periodic, frequent comparisons, records can undergo one or more waiting periods between comparisons. Waiting periods can be many time periods, for example, approximately one day, such that a set of comparisons occurs daily for each record. During waiting periods, additional records can be received. At the end of a waiting period, a first received record can be compared to one or more second records. Based on these new comparisons, reports can be generated for subscribers. Reports can contain analysis of records submitted after a first record and during the course of receiving additional records. This feature of embodiments of the present invention enables continuous monitoring of loan transaction to
- 2017838 l.DOC fi detect fraudulent activities occurring within close time periods. Comparisons can occur periodically by repeatedly comparing already received records to newly received records. [00017] Periodic comparisons can continue until the termination of a predetermined holding period. During a holding period, received records can be retained in a database in an active state, meaning periodic comparisons continue. The holding period can be, for example, approximately 30 days to approximately 90 days in some embodiments. For example, each stored or received record can be compared daily to other stored or received records for 61 days after the record is submitted to a database operator or entered into the database. Other holding period lengths may also be used in accordance with embodiments of the present invention (for example, ranging between 120 days to twelve months).
- fraud detection system embodiments of the present invention can be configured to receive a plurality of records from a plurality of users (e.g., contributory subscribers). Periodically in bulk, or individually as they are received, records are stored in a database. After a new record is received, it can be compared to records preexisting in the database. A database operator alone or in combination with a reporting program can report comparison results to subscribers submitting records and to subscribers that records submitted records preexisting in the database. The results in such reports can be ordered according to a two-tier weighted algorithm. [00019] As used herein, a database operator need not be a human being.
- the database operator can be a computer system, a computer program, one or more modules of a computer system, or a combination thereof.
- a receiving module can receive records submitted from outside sources, including system subscribers or end users.
- a comparing module can compare records to each other.
- a reporting module can report results to subscribers.
- a database operating module can conduct one or more of the tasks performed by the database operator.
- a fraud detection system to detect fraud in a financial loan application process by analyzing a plurality of data from multiple
- the system can generally comprise one or more interfaces (e.g., communication interfaces) to receive data, a database, and a processing module.
- the interfaces can be configured to receive data from a plurality of unique data sources.
- the data sources can comprise information received from unique end users and existing unique databases.
- the data can comprise data records comprising at least one of information related to a financial loan application, parties involved in a loan transaction, and property involved in a financial loan.
- the database can be configured to receive data from a plurality of unique data sources and to store data with an identifier configured to distinguish data received from unique data sources.
- the database can include a processing module or may be operatively coupled to a processor configured to implement a processing module.
- the processing module can be configured to determine if one or more data records contain one or more matching data fields and flag data records containing one or more matching data fields. Matching information can be stored in a match table, which can be a stand alone memory or associated with the database. The processing module can be further configured to determine if one or more of flagged data records contain different information in one or more other data fields contained in the data records. And data records flagged to contain different information can be stored in the match table. [00023] Fraud detection systems according to the present invention can also include other features. For example, matching data fields can comprise information about property involved in a financial loan and at least a party involved in a loan transaction.
- a system can also include a reporting module configured to report information concerning data records containing one or more matching data fields and information concerning the flagged data records containing different information in the one or more predetermined data fields.
- a reporting module can be further configured to order information contained in a report at least partially based on different information contained in the one or more data fields and a magnitude of difference.
- a processing module further can be configured to place received data records in an active state for a predetermined holding period and periodically analyze active- state data records relative to existing data and data received after the active-state data records. Predetermined holding periods can range from approximately one day to approximately one year.
- a processing module can be configured to determine if one or more of data records contain substantially similar addresses for a real estate property involved in a loan transaction.
- a fraud detection method can generally comprise providing a database operatively configured to receive data comprising details concerning a financial credit or loan application and to store received data as one or more first data records.
- a fraud detection method can also include configuring a database to receive data from a plurality of unique data sources and store data with an identifier for distinguishing the unique data sources. The data can comprise at least one of, one or more parties involved in a loan transaction, and property involved in a financial application process.
- a fraud detection method can also include providing a data comparison module operatively configured to flag the one or more first data records containing similar data in one or more predetermined data fields by storing matching information in a table.
- a fraud detection method can include configuring a data comparison module to determine if the flagged data records comprising at least one data field with similar data comprise one or more other data fields having inconsistent data by storing inconsistent data in the table.
- Fraud detection methods according to the present invention can also include other features.
- a method can include configuring a database to provide reports comprising details related to flagged data records comprising at least one data field with similar data and details related to flagged data records comprising data fields with inconsistent data.
- a method can also include configuring a database to receive data from a plurality of end users and the one or more databases via a communications network.
- a method can also include providing a data comparison module operatively configured to flag data records containing similar data in one or more predetermined data fields by determining if the one or more data records contain data fields having similar property address information or similar borrower information.
- Fraud detection methods of the present invention can also include additional aspects.
- a method can include configuring a database to provide reports to a user with data ordered based on results of a two-tier weighted algorithm.
- a method can include holding data records in the database for a predetermined holding period and configuring the data comparison module to compare existing data records to data records received after the existing data records at predefined intervals.
- a method can also include operatively configuring a database to receive lender originated data and user incident report data.
- a method can include configuring a data comparison module to receive professional license information from one or more existing databases to determine if a licensed party involved in a loan transaction is
- computer-readable media containing code for execution by a processor are provided. These embodiments can include stored computer readable instructions to execute a method for detecting fraud and/or implement a system for detecting fraud.
- code can include instructions for providing a database configured to receive data from a plurality of unique data sources and associate received data with an identifier to distinguish the unique data sources.
- the data can comprise data records having data fields.
- the data records can comprise information related to a financial loan application, parties involved in a loan transaction, and property involved in a financial loan.
- Another instruction in the code can include determining whether one of the data records has a data field containing common data to at least one other data field contained within at least one of the other data records.
- Another instruction in the code can include determining whether data records having at least one data field in common have other data fields having disparate data.
- Still yet another code instruction can include providing a report comprising data records based at least partially on the identifier. The report can include a listing of data records with data fields having common data and data records containing data fields having disparate data.
- Computer-readable medium embodiments having stored code can also include additional aspects. For example, within the code instructions can be provided such that at least a portion of data records contain mortgage loan application information submitted by unique users and at least another portion of the data records contain information obtained from pre-existing databases containing borrower information. Such code can enable a dual pipeline feature as discussed herein.
- Another exemplary aspect includes providing code instructions such that data fields are reviewed for common data, including property address information, to determine if a single property is subject to multiple loan applications.
- Code instructions can be provided on a medium to repeatedly determine on a pre-determined basis whether data records have data fields containing common data relative to other data fields contained within other data records, and whether data records having data fields in common also have data fields having disparate data.
- all embodiments of the present invention can include one or more of the advantageous features discussed herein.
- one or more embodiments may be discussed as having certain advantageous features, one or more of such features may also be used in accordance with the various embodiments of the invention discussed herein.
- discussion contained herein may, at times, focus on mortgages and mortgage application processes, embodiments of the present invention can also be used in other financial and financial application settings.
- exemplary embodiments may be discussed below as device, system, or method embodiments it should be understood that such exemplary embodiments can be implemented in various devices, systems, and methods.
- FIG. 1 illustrates a block diagram of a fraud detection system according to some embodiments of the present invention.
- FIG. 2 illustrates an information flow diagram in a fraud detection system according to some embodiments of the present invention.
- FIG. 3 illustrates comparing one record to one or more records in a fraud detection system according to some embodiments of the present invention.
- FIG. 4 illustrates a fraud detection method according to some embodiments of the present invention.
- FIG. 5 illustrates an information flow diagram for fraud detection system users according to some embodiments of the present invention.
- FIGS. 6A-6F illustrate exemplary reports provided by a fraud detection system according to some embodiments of the present invention.
- FIG. 7 illustrates a method of fraud detection according to some embodiments of the present invention.
- FIG. 8 illustrates a method of an incident submission process according to some fraud detection embodiments of the present invention.
- FIG. 9 illustrates a method of authenticating and verifying entities and professionals involved in a financial application process according to some embodiments.
- embodiments of the present invention are described below for detecting signs of fraud in mortgage loan applications. Embodiments of the invention, however, are not so limited. Rather, embodiments of the invention can be used to detect fraud or to track patterns, similarities, or dissimilarities in many fields. For example and not limitation, embodiments of the present invention may be used to detect fraud in credit card applications, employment applications, and personal property loan applications. [00040] The various embodiments of the present invention assist users in detecting fraud.
- embodiments of the present invention are configured to alert users of potential fraudulent activity so that users can further investigate the circumstances to mitigate fraud.
- embodiments of the present invention can enable the mortgage lending community to more accurately determine the authenticity of the borrower/applicant wishing to obtain a mortgage for real estate.
- fraudsters may falsify information related to names, social security numbers, or both in an effort to create false mortgage transaction and escape detection.
- a fraudster can assume an identity and begin establishing a historical record of credit as a part of the process for stealing the identity.
- multiple names can be used with a single social security number in an effort to thwart off detection. The alternate names are submitted to multiple lenders to avoid being spotted during the process. Lenders are currently vulnerable to this type of attack because there is no system that enables identity variances across multiple transactions while verifying information provided by borrowers to lenders in a loan transaction.
- FIG. 1 illustrates a diagram showing components of a fraud detection database system 100 according to some embodiments of the present invention.
- the database system 100 can generally comprise at least one database manager 110, at least one database operator 120, one or more users 130, and a database 140.
- the database system 100 may also comprise one or more external databases 150 for providing additional information to the database 140.
- the database 140 (and/or the database operator 120 or the database manager 110) can be configured to access one or more external databases 150 to provide additional data to the system 100 to provide enhanced fraud detection capabilities.
- the illustrated components of the system 100 can be networked together. Such networking enables the system's 100 components to exchange and share data.
- users 130 are preferably networked with the database operator 120 via a network, such as network 160.
- Network 160 may be a private or public network.
- the network 160 can include the internet. This configuration enables users 130 to access and utilize fraud detection via an internet, web-based interface. In this manner, users 130 can access an internet portal to query and search data maintained in the database 140 to obtain desired information.
- the database operator 120 can include various processing modules for automated querying of the database and for reporting results to users. Such reporting can occur based on user defined search queries and user defined search frequencies.
- the network 160 can also be a private network and also have both public and private features to enable implementation of varying security protocols.
- the components in the system 100 are shown as stand-alone components, certain of the components may be integrated together.
- one or more of the database manager 110, the database operator 120, and the database 140 can be integrated together in a single physical device. In such an embodiment, a single physical device enables a centralized database system 100 at a single location.
- one or more of the database manager 110, the database operator 120, and the database 140 can have various components capable of implementing required functionality.
- the database 140 may have one or
- each system component can have various operation functions.
- the database manager 110 can initialize and maintain the database 140.
- the manager 110 can perform, or cause to be performed, maintenance required by the database 140.
- the manager 110 can also ensure that users 130 execute legal and other documentation desirable for participation in the database system 100.
- the database operator 120 can be configured to perform database tasks as desired.
- the database operator 120 can conduct queries on the database 140, and in response to the queries, the operator 120 can receive data from the database 140. Based on data received from the database, the operator 120 can output reports to users 130. The database operator 120 can also accept data submitted by users 130 and can transfer the submitted data to the database 140. As discussed herein, users 130 can provide data related to or concerning financial loan transaction applications (e.g., mortgage loan information and fraud incident information). The database operator 120 is also preferably configured to enable and accept batches of data from many different users 130.
- the system 100 is contemplated, in some embodiments, as a very dynamic system that is on a real time basis receiving information from many different sources and reporting information to users in response to queries. Use of such raw, current data for analysis can help to enable real time detection of fraud.
- the database operator 120 can also be configured to have numerous operational characteristics.
- the database operator 120 can include web service functions to enable an internet interface between users 130 and the database. Inclusion of web service functions enables users to access the fraud detection system 100, to query the database 140, and obtain data in an effort to learn additional information about parties to a financial application.
- database operator 120 can include various processing modules configured to search and analyze data according to predetermined data tests. Such data tests can compare data from various sources on a wide-scale basis and provide results of the comparisons to users.
- the database operator 120 can be, for example, a module of the database 140 capable of executing computer code. Additionally, and/or alternatively, the
- DOC 14 database operator 120 can be a device, a segment of code, a computer system adapted to operate in conjunction with the database 140, a database operating module of a computer system, and/or a combination thereof.
- the database operator 120 can comprise a plurality of modules, such as, for example, a receiving module comprising data interfaces for receiving records, a comparing module for comparing records, and a reporting module for reporting results.
- the database operator 120 can also provide an interface enabling users to submit and obtain data to the database 140.
- Users 130 of the system can be various types of people or entities desiring assistance in detecting fraud.
- users 130 can be subscribers who are entities, such as mortgage lenders, banks, or mortgage brokers, who subscribe to the database system 100.
- Users can also include mortgage bankers, financial lending institutions, real estate agents, appraisers, closing agents, mortgage portfolio investors, home builders, title companies, loan servicers, loan consolidators, accountants, attorneys, banks, direct consumers, other mortgage related agencies, and non-mortgage related agencies.
- Users 130 may also include law enforcement personnel and others involved in investigating and prosecuting fraudsters.
- users can also provide information to the system 100.
- users 130 can submit records to the database operator 120 for submission to the database 140.
- Such records can include mortgage loan application information (e.g., Uniform Residential Loan Application - Form 1003) and also fraud incident information.
- the system can advantageously obtain information from users who interact with mortgage-related transactions on a daily basis thereby providing a dynamic system capable of obtaining current and fresh data. This enables users to cross check their data with other users and enables the system 100 to consider data from numerous users in an effort to flag possible fraud conditions.
- Users 130 can also receive reports of potential fraud from the database operator
- the database operator 120 enables users to log in to the system 100 and view transactions in the user's internal pipeline and transactions external to the user's pipeline.
- lender A may desire to track loan transactions in its pipeline for potential fraud activity and also track loan transactions external to lender A's
- lender A can track loans in the pipeline of other lenders to see if any loan transactions in lender A's pipelines contain similar person data to transactions of other lenders.
- the dual pipeline feature advantageously enables users (such as brokers and lenders) to cross check transactions in their pipeline with transactions in one or more users' pipelines. As a result, this feature can help inform users if their clients are also interacting with other users who have submitted data to the database 140.
- the database 140 maintains the records submitted via the database operator 120.
- the records can comprise data records (or data sets) with various data fields.
- the data records have a uniform format with uniform data fields. Such uniformity enables the database operator 120 to compare and contrast data contained in the data records to determine existence of possible fraudulent activity.
- the database operator 120 and/or the database 140 can validate received data records to ensure appropriate format.
- the volume of records submitted to the database 140 can be, for example, upwards of approximately one thousand per day per user 130.
- the database 140 is scalable to enable receipt of less or more records on a daily basis as desired.
- the database 140 is preferably configured to maintain this volume of data.
- the database 140 is preferably also a storage facility capable of storing large amounts of data.
- FIG. 2 illustrates an information flow diagram in a fraud detection system according to some embodiments of the present invention.
- FIG. 2 specifically illustrates a flow diagram 200 of information from various system users 130 to a fraud detection system 100.
- a first borrower 210 borrower 1
- borrower 1 210 discloses certain application information to broker 220.
- broker 220 discloses this information to a user 130, lender 1 230.
- Lender 1 230 submits to a database operator 120 a record relating to and containing information contained in borrower l's loan application.
- the database operator 120 received this record and enters the record into the database 140.
- the provided record may be used to update or refresh an existing record.
- FIG. 2 also illustrates additional users 130 providing data to the fraud detection system 100. Indeed, FIG. 2 shows that a second borrower 240 provides mortgage loan
- 2017838_l.DOC 16 application directly through a second lender 250.
- the second lender 250 can submit borrower 2's 240 application information to the system 100 via the database operator 120.
- the database operator 120 can submit the information to the database 140 as a new record (or an updated or refreshed version of an existing record).
- a plurality of users 130 can provide information to the fraud detection system 100 at varying rates as desired.
- all users 130 can be configured to communicate with other users through the fraud detection system 100.
- users 130 may submit potentially confidential information to the database 140, it may be desired that involved parties enter into agreements for protection.
- users 130 can certify that they understand the database system 100 reports tips and leads only, and that decisions of whether to accept or deny loan applications should not be made solely based on such reports. Users 130 can also certify that their use of reports will comply with applicable laws and regulations, including the Fair Credit Reporting Act ("FCRA"). Further, it may desired to have users 130 indemnify the database operator 120 and the database manager 110. The database manager 110 can ensure that these legal documents are executed.
- FCRA Fair Credit Reporting Act
- the corresponding record submitted to the database operator 120 by a subscriber 130 can consist of originator information as well as borrower information from a loan application, such as the Uniform Residential Loan Application.
- Originator information, or originator data can include data on an entity originating the loan application.
- originator information can include the name of a mortgage broker and other information related to and unique to the mortgage broker.
- the following list represents a non-exclusive list of data that may be included in originator information submitted to the database operator 120 in a data record:
- Borrower information comprises data supplied by the borrower in a loan application. This data can be based on documentation from borrowers.
- the following list presents a non-exclusive list of borrower information that can submitted by subscribers 130. Not all listed items need be submitted by every subscriber in every instance, and additional data may be submitted in addition to or in lieu of the listed items.
- Users 130 can provide data to the system 100 in a variety of manners.
- data can be provided on a batch basis and in others on a single record basis. Transfer of batch data can occur at random times or in accordance with a predetermined data
- 2017838_l.DOC 21 transfer protocol For example, some users may desire to transfer loan application information on a daily basis as loan transactions are initiated and as loan transactions move through a lifecycle process. Alterative manners of providing data to the detection system include submitting incidents of fraud data and commenting on existing fraud scenarios. By enabling users to submit transaction data to the system 100, the system 100 can advantageously receive fresh raw data to analyze for potential fraud occurrences and provide reports indicating possible fraud to system users.
- embodiments of the present invention receive data from users, the data can be analyzed for possible occurrences of fraud. When such fraud occurrences are determined to exist for one or more transactions (e.g., a mortgage loan application), embodiments of the present invention can flag such occurrences so that users can further investigate flagged transactions.
- fraud detection systems according to embodiments of the present invention generally compare received records to each other in an effort to locate possible fraudulent activity.
- embodiments of the present invention can also be configured to access already existing external databases to obtain additional data for comparison to received records from users. Data comparison of data records can be done on a data field basis to determine if disparate data exists and to determine a magnitude of difference between data fields.
- FIG. 3 illustrates comparing one record to one or more records in a fraud detection system according to some embodiments of the present invention.
- FIG. 3 shows a diagram showing how data records 310 are compared to other records in a database according to some embodiments of the present invention.
- a first exemplary record 312 can be submitted to the database 140 from a user 130 via the database operator 120. After submission, and periodically for a predetermined period, the first record 312 can be compared to other records 310 in the database 140 (other records are shown as Records 2, 3, 4, through N).
- fraud detection systems can determine if applicant information differs from existing or other information sources across many numbers of records. By performing such checks, the fraud detection system can flag possible fraud occurrences and report the same to users.
- comparison of records can include multiple predetermined checks.
- comparing the first record 312 to another record 310 can generally comprise two checks. These checks can be, but need not be, performed by the database operator 120 or a processing module implemented in one or more components in a fraud detection system.
- a first check can include determining whether records are matches, or preliminary matches.
- Preliminarily matching records 310 can comprise similar information in certain data fields. For example, and not limitation, records 310 with the same borrower name, the same property location or address, and/or the same social security number can be deemed matches.
- other data records can be checked for consistent data in certain data fields (such as those in the above provided lists).
- Data records having consistent or matching data in one or more predetermined fields can be flagged for subsequent processing. In the event that records do not contain consistent data in one or more predetermined fields, no further comparison may be required. Alternatively, data records having inconsistent data in data fields may be flagged for subsequent processing.
- a second check can determine if records flagged as having consistent data in one or more data fields have inconsistent data in various other data fields. For example, a second check can determine if records flagged as matches include inconsistent data, such as contradictory data in such fields as appraisal value, loan amount, borrower SSN, and borrower account/income values. If inconsistent data is found in one or more of the records 310, the records 310 can be flagged. For example, and not limitation, if a first record 312 includes a borrower income of $55,000, and another record 310 includes a borrower income of $155,000, the two records 310 can be flagged. Such disparate data may be a sign of a fraudulent occurrence, and in response, the database operator 120 can return flagged records 310 to subscribers 130 in the form of reports. Similarly, the database operator 120 can also return matches to subscribers 130 in reports.
- inconsistent data such as contradictory data in such fields as appraisal value, loan amount, borrower SSN, and borrower account/income values.
- FIG. 3 may suggest that comparisons are implemented through a direct comparison between records 310, this particular implementation is not required.
- digital references to the records 310 can be sorted over borrower social security numbers, borrower names, property locations, or any combination of these three (as well as the above-listed data fields). Accordingly, determining matches between records can include
- FIG. 4 illustrates a method 400 of detecting fraud according to some embodiments of the present invention.
- method 400 can be performed in various orders (including differently than illustrated in FIG. 4), additional actions can be implemented as part of a method embodiment, and that some actions pictured in FIG. 4 are not necessary.
- FIG. 4 may be discussed herein as including certain other actions, these certain other actions may be carried out in various orders and/or as parts of the other actions depicted in FIG. 4.
- Method embodiments of the present invention such as the one depicted in FIG. 4, may be implemented with the fraud detection devices and systems discussed herein.
- the method 400 generally initiates by receiving data from one or more data sources.
- the received data can be stored in one or more databases.
- Data sources can include one or more contributory users as well as one or more existing databases. Records may be received on a routine frequent basis (e.g., daily at a set time or bi-weekly) or as desired (e.g., on a random, unscheduled basis). Indeed, users may submit records on a nightly basis and a fraud detection system may access and/or obtain information stored in one or more other databases as needed. It is contemplated by the inventors that many data records (e.g., 1000 or more per day) will be received to enable the various fraud detection methods to assist in fraud detection.
- the method 400 can also include verifying that a submitted data record is submitted by a valid subscriber by checking a subscriber registry.
- the method 400 may also include verifying that the received data is compliant with a predetermined data record format. Verification ensures that data originating from various and sometimes disparate sources, is in a data record format to enable data comparison and analysis.
- the specific format of a data record (which can include any of the data fields listed herein as well as other desired data) is not important. What is important is that data records have the same format or have corresponding data fields. For example, in some embodiments it may be desired to have data records with the same data fields within the data record (e.g., same data record layout). This configuration can readily enable comparison of data fields within data records and accurate, efficient analysis of data fields.
- the method 400 can also include an initial review of records. The initial review can include iterating through many records as illustrated at 405. In some embodiments active
- 2017838_l.DOC 24 and inactive records may be reviewed while in other embodiments only active or inactive records may be reviewed. Records may be changed from active to inactive or vice versa depending on a number of factors, including user selection, data threshold, or time stamp threshold. In addition, active or inactive status may be used to remove and/or add data records in and out of a user' s internal pipeline. Records may also be updated during a lifecycle of an application process.
- the initial review may also include data record verification as discussed above. The process of initial review preferably yields a grouping or set of records (e.g., all active records) for further analysis. [00068] Further analysis can include searching selected records to determine if the records have one or more common data fields.
- Such data fields may be deemed critical data fields for fraud detection purposes in that the data within these fields holds unique significance relative to other data fields.
- the method 400 can include a further analysis to determine whether records have similar property address information. To do this, property address fields in each record are compared with other records.
- the method 400 may also include searching for records with, for example, similar borrower names or borrower social security numbers. Still yet, the method 400 can include searching for any combination of critical data fields, in addition to those discussed above, as desired by a user.
- the method 400 can also include standardizing records. Standardization of records can occur at any time during implementation of the method 400. For example, standardization can occur prior to or after record verification and also prior to of after comparison of records. Standardization can assist in ensuring that data is in similar format thereby enabling efficient verification and comparison. Standardization can include converting received data to a standard form. As an example of standardization, address data (e.g., borrower addresses) can be converted to United States Postal Service format. This way, for example, St. and ST are converted to STREET.
- address data e.g., borrower addresses
- name prefixes e.g., Mr., Ms., Mrs., etc.
- suffixes e.g., Sr., Jr., Ill, etc.
- a used standard for data is not important; rather, ensuring that data is standardized for ease of comparison is important for embodiments utilizing data standardization.
- data standardization may only occur on some data fields while in others, data standardization may occur on all received data fields. Standardized data elements will, preferably, render the data in the same format.
- the method 400 can include determining whether preliminary
- DOC 25 matches between data fields in various data records are found.
- a fraud detection system can determine whether one or more matching records are found for a first record as a result of comparing predetermined data fields of various data records.
- a preliminary match analysis can determine if data records have similar SSNs, borrower names, property addresses, or any combination thereof. If such matching records are found, the method 400 may denote that one or more preliminary matches have occurred. If no matching records are found, the method 400 can proceed to 450. And if matching records are found, the method can proceed to process 418 to determine any potential secondary matches. [00071] As shown, process 450 inquires whether any new of unreviewed records exist. If such records exist, the method 400 can initiate again at 405.
- the method 400 may end and await receipt of additional records and/or user instruction. If a preliminary match to other records is not found, the method 400 can then return that a loan application has no corresponding matches. As a result, the method 400 may deem a loan application to be a low risk application. In some embodiments, low risk applications may not be searched again and may also be flagged for reporting or non-reporting low risk applications to users depending on user reporting preferences.
- process 418 can include several processes to determine potential secondary matches. For example, if a preliminary match is found across property address fields at 415, the method 400 can compare each field of matching records.
- the secondary match analysis 418 is advantageously configured to determine if any records having preliminary matches (e.g., have matching property addresses) have data fields with disparate information (e.g., different appraisal values).
- process 420 can include comparing records to locate data fields with disparate information.
- a matching table can be provided for storing matching records, or references or pointers to such records. The matching table can also be configured to store information about matches, including whether a matching record was found in an external database.
- the method 400 can perform a field-by-field comparison to determine matching records at 425.
- process 425 can provide matching indicators in a matching table. That it, if corresponding fields of records match exactly, an indicator of such exact match can be stored. For example, an "E"
- 2017838_l.DOC 26 (denoting exact match) can be stored corresponding to an applicable field and record in a matching table. As another example, if data fields differ, then this can be indicated by storing an "N" (denoting no match) in an appropriate place in a matching table. And as another example, if data fields vary, a variance indicator can be recorded to indicate a degree of difference. The variance indicator may be used as a multiplier in a two-tier weighted algorithm as discussed below. Typically, a larger data variance in the data yields a larger variance value. For example, for an exact match (“E"), a variance value may be a zero (0), and for no match (“N”), a variance value can be a three (“3").
- the method 400 can also include applying weights to score loan application data records at 430. More specifically, one or more data fields in one or more data records can be weighted for importance. For example, in some embodiments, data fields of high importance for detecting fraud can be given higher weights relative to other data fields. Fields containing specific identifying information, such as borrower name or SSN can be provided with high weights, or the highest weights. Only fields with weights greater than zero need be compared for records with matching property addresses. Fields can be afforded a zero weight for strategic reasons or because those fields are not likely to indicate fraud.
- the weights in combination with the variance between corresponding fields, can determine a relative significance of fraud (e.g., a score denoting potential fraud possibility).
- a relative significance of fraud can be used to determine a likelihood of fraud and also enable a user to take steps to further investigate.
- weights can be implemented with a multiplier value.
- the method 400 can also include determining a score for a data record at 430.
- Data records as discussed herein can be loan applications so the method 400 can determine a loan application score indicative of possible fraud associated with a loan application.
- a provided weight value can be multiplied by a variance to determine a field score.
- an "E” value can be afforded a lowest variance value for a particular field, while an "N” value can be afforded a highest variance value for a field.
- Other variance values may also be utilized.
- Data field scores can be summed for a corresponding data record to determine a data record score.
- the method 400 can also include providing one or more fraud scenario based on variance amounts (e.g., determined "N" and "E” results). Table I below
- the method 400 can also include similar scoring for additional data records by analyzing additional record matches at 435. As shown, process 435 can determine whether one or more additional records with a same property address, for example, as a first record are found in a database. If so, a set of field-by-field comparisons can occur for each such additional record. After all matching records have been found and compared field-by-field to the first record, the match scores can be summed to determine a total loan score at 440. [00077] The loan score can denote a risk of fraud and also be used to determine a recommended level of due diligence.
- loan scores and due diligence levels can be provided on a numbered range (e.g., 1 to 10, or 1 to 100) and/or with "HIGH,” "MEDIUM,” or "LOW" labels.
- loan score and due diligence level information can be provided to a user via a web-based interface enabling varying use of color indicators (e.g., red, yellow, green, etc.).
- such information can be provided for each loan application in a user's loan application pipeline and also summarized based loan score and/or diligence level.
- Such grouping enables users to focus resources as needed on desired loan application groupings in an efficient manner.
- the method 400 can score loan applications to assist users in detecting mortgage fraud. Based on scoring results, and as shown as 445, the method 400 can also determine an alert status for a one or more data records (or loan applications) in a lenders pipeline. If the loan score is above, below, or consistent with a predetermined threshold or threshold range, the method 400 can flag one or more loan applications to alert one or more users who submitted a loan application.
- the flags can be alerts can be in the form of emails, text messages, blog entries, or many other communications. Preferably, the flags provide analysis information to users so that users can then determine any potential next steps in detecting any fraud associated with a flagged data record.
- the method 400 may continue processing new or additional records at 450. If no such records are located, the method
- 2017838_l.DOC 28 400 can end or await further instruction. It is currently contemplated that fraud detection method embodiments of the present invention, like method 400, can review numerous data records in very short time frames all while continuing to receive additional, new, and/or updated records. Thus, embodiments of the present invention are contemplated as being dynamic in that record review of raw data continues on numerous records at any one time in addition to records continuously being added or updated with raw data information. Analysis of such raw data in accordance with embodiments of the present invention advantageously enables review of realtime data thereby yielding accurate and timely results to users. [00080] Referring back to FIG. 3, the database operator 120 can compare records 310 for differences, as described above, and can alert subscribers 130 of these differences.
- the fraud detection system 100 can be configured to be primarily concerned with inconsistencies between loans applications, and can also be configured to report multiple similar applications that are not inconsistent. For example, users might desire to be made aware of loan-shopping. Loan shopping can be suggested by multiple consistent loan applications for the same borrower on the same property. If a user is aware of loan-shopping, then the subscriber can be prepared to make a competitive bid for the borrower's business.
- loan applications having certain fields with consistent data but having certain other fields with inconsistent data can be a fraud warning sign.
- Fraudulent loan applications typically include different borrower data across multiple loan applications for the same property. For example, and not limitation, records containing the same borrower name but different appraisal values can be deemed inconsistent.
- the database system 100 can be capable of further analyzing results, but at the least, the system 100 can preferably return reports indicating flagged records. Upon receipt of such flagged records, users 130 should perform due diligence to determine the cause of flagged inconsistencies.
- Inconsistencies between records 310 need not be the result of fraud. For example, inconsistencies may arise due to human error, such as typographical errors.
- embodiments of the present invention can also be configured to determine a magnitude of difference between inconsistent data. Determining magnitudes of difference for certain data fields can also be indicative of possible fraud. Indeed, a user 130
- 2017838_l.DOC 29 might perform due diligence to determine whether a magnitude difference above a certain threshold comports with possible signs of fraudulent activity.
- embodiments of the present invention can provide data elements triggering the variance. This enables users to determine if a typographical error or a true misrepresentation is a cause for the variance. For example, if a submitted appraisal amount is $345,700, a first matched loan triggered a variance to display $354,500, and a second match loan displayed $374,500. In this scenario a user can determine that both returned variances could be typographical errors due to transposing of numbers in the returned appraisal amounts. [00083] Even if no typographical error occurred, there may be a plausible, legal explanation for the variance.
- records 310 include requested loan amounts of $1,295,340 in one record 310 and $1,900,000 in the other. While this may be the result of fraud, the applications might also have been submitted at different times. The borrower' s knowledge with respect to his own funds might have differed greatly between the submission times. A user 130 can be informed of this and then perform due diligence to verify the correct requested loan amount.
- users 130 can monitor records over a lifecycle of a loan.
- the database operator 120 can continue to monitor records with newer received records to continually monitor for possible fraudulent activity.
- records 310 can be compared to other records 310 periodically for a predetermined holding period. Indeed, in some embodiments a record 310 can be compared to other records 310 daily for a holding period of approximately 30 to approximately 90 days. Alternatively, additional exemplary holding periods can be 61 days and can range from 1 day to a complete year. Users can modify holding periods per submitted transactions by providing such search query information to the database operator.
- Embodiments of the present invention also enable users to modify loan transactions provided in their respective pipelines.
- fraud detection system 100 can be configured to maintain various pipelines of applications segregated by users 130. This advantageously enables users to monitor their borrower-specific applications with an internal pipeline and then applications of other users via an external pipeline.
- fraud detection system 100 embodiments of the present invention enable users to modify transactions
- 2017838_l.DOC 30 maintained in their pipelines.
- users can provide this information to the database operator. In doing so, users can modify the number of transactions contained within their pipeline.
- fraud detection system embodiments of the present invention can be configured to provide updated information to users when data in a data record changes or corresponds with another data record maintained in the database. These features of embodiments of the present invention enable users to maintain records in their pipeline as desired and also control how reports are provided to them by the system 100.
- a fraud detection system 100 can perform credential checks on those involved in a loan transaction.
- a database operator 120 can verify that professionals included in originator information hold valid credentials.
- a credentialed list can be maintained in the database 140 for each subscriber 130.
- the database operator 120 and/or database 140 can be configured to access one or more external databases to obtain credential information.
- a credentialed list can include, without limitation, a list of known mortgage brokers and licensing information.
- Originator information can be compared to one or more credentialed lists for one or more users 130. Additionally or alternatively, originator information can be compared to a system credentialed list, which can be maintained for the benefit of all or a set of the users 130. If originator information fails to match any item of a relevant credentialed list, users 130 can be alerted.
- FIG. 5 illustrates a flow diagram of information from the fraud detection database system 100 to users 130 according to some embodiments of the present invention.
- the database operator 120 can retrieve data from the database 140, preferably by way of queries to the database 140.
- the database operator 120 can send reports to the users 130.
- the database operator 120 can also send reports to the database manager 110, which the manager can use to ensure the database system 100 is running as desired.
- the database manager 110 can implement administrative checks and processes to ensure that a fraud detection system is operating as desired.
- Reports to users 130 can list all or a subset of preliminary matches, and can list all or a subset of flagged matches. Summary reports can highlight inconsistent data of flagged
- a user 130 can receive multiple reports on a single loan application. Each report can be cumulative or, alternatively, each report may list only results of comparisons to new records 310.
- a report can include a due diligence level indicating a level of suggested diligence.
- a report may also include an identification verification score providing a score indicative of whether provided names and biographical information are consistent with various other data records.
- Fraud detection system embodiments according to present invention can deliver and/or provide reports in electronic form.
- reports can be provided in an email format and/or in response to real-time queries. If results are reported via a computer program in which a user 130 logs in to view results, the program can ensure that users 130 have an opportunity to sufficiently view the report. For example and not limitation, results can remain viewable until a user 130 takes an affirmative action, such as closing a window or deleting a report in a web-based interface. Users 130 can choose whether to receive cumulative reports or to receive only new results.
- a user 130 can notify the database operator 120 when a loan application is removed from the subscriber's pipeline.
- the database operator 120 can remove the associated record 310 from the database 140 so that no further reports to that user 130 include comparisons with one or more associated records 310.
- Other users 130 may continue to receive notification in their reports of inconsistent information between their applications and the removed loan application.
- Reports can list results in a particular order based on preferences of the industry or of individual users 130. [00091] The order can be determined based on a two-tier weighted algorithm for each element of borrower information in the records 310. Such an industry-generated algorithm may be derived based on feedback from top lenders and the Mortgage Bankers Association. In a two- tier weighted algorithm, inconsistencies in certain data elements can be more significant than inconsistencies in other data elements in identifying potential fraud.
- an inconsistency in requested loan amount or appraisal value can be more significant than an inconsistency in current employer address.
- a borrower's SSN may be a critical data element in determining a valid identity, acquiring credit history, verifying employment and income, etc. Because identity fraud and/or theft generally requires some level of falsification of an SSNs, SSNs can be weighted as a more important data element (e.g., as a first tier weight).
- the first tier in the weighted algorithm can consider the significance of the inconsistent data elements.
- the second tier can weigh the magnitude of the inconsistency. For example, a requested loan amount difference of $145,000 and $154,000 is less significant than a requested loan amount difference of $145,000 and $300,000.
- a high risk alert can be assigned regardless of a total loan score.
- the user 130 may implement tools to research and verify the true income of the borrower. For another example, if a user 130 sees multiple loan applications for the same property but with different borrower names, the cause could be a typographical error. If this is the case, the user 130 might perform due diligence in verifying the correct spelling of the name.
- a user 130 sees multiple loan applications across different lenders for the same property but with different names, the user 130 might suspect a shot- gunning scheme. In that case, the user 130 might verify the authenticity of the loan application.
- the database operator 120 can perform preliminary analysis on inconsistent preliminary matches. For example and not limitation, reports to users 130 can categorize inconsistencies to highlight the possibility that certain fraud schemes are underway.
- FIGS. 6A-6F illustrate exemplary reports provided by a fraud detection system according to some embodiments of the present invention.
- the first row 610 of the reports in FIGS. 6A-6F lists headings for the items in the second row 620.
- the first item of the first row 610 states "Loan ID.” Accordingly, the first item in the second row of the first column gives the
- the database operator 120 can return a preliminary fraud analysis in reports.
- item 660 gives a possible fraud scheme based on a preliminary analysis of the inconsistencies in the records.
- a possible fraud scheme can be returned based on the results of a number of IF-THEN-ELSE tests corresponding to known fraud schemes. These tests are based on data fields contained with data records.
- Such returned fraud schemes can include those discussed above and also those discussed below in more detail (for example, see TABLE I and associated text).
- Item 670 gives the potential risk that the records referenced in the report represent a fraud scheme. Additionally, as shown only in FIG. 6D, the report can list additional useful information 680, such as non-credentialed and/or sanctioned professionals.
- the database operator 120 can produce comprehensive aggregated reports from the database 140 for the database operator's own use or for the database manager 110.
- comprehensive reports can list a number of mortgage applications processed, the number of different appraisals by a single appraiser, a number of different appraisals for the same property with different appraisers, the number of times a potential shotgun fraud was identified, the number of times a flip fraud scenario was identified, and many other data.
- Reporting can also be tailored to user's pipeline settings. In this manner, for example, reports can include predetermined formats based on a user's pipeline settings and may only report testing data of a user' s pipeline.
- FIG. 7 illustrates a method 700 of fraud detection according to some embodiments of the present invention.
- method 700 can be performed in various orders (including differently than illustrated in FIG. 7), additional actions can be implemented as part of a method embodiment, and that some actions pictured in FIG. 7 are not necessary.
- FIG. 7 may be discussed herein as including certain other actions, these certain other actions may be carried out in various orders and/or as parts of the other actions depicted in FIG. 7.
- the method 700 may initiate in certain embodiments as shown at 705 by providing an interface for receiving data. The data can be transmitted to and received into a database for processing in an effort to detect fraud.
- the data may include financial transaction information including, mortgage loan information.
- the data may also include information concerning borrowers, lenders, and/or entities.
- Data can be submitted by users or data can be obtained by accessing one or more databases that may be publicly or private databases.
- the interface can be a web-based interface. Such an interface enables users to submit information (e.g., mortgage loan information) for processing via a web browser.
- a web- based interface can also enable transmission of information over the internet.
- the interface may be provided as a software application capable of submitting data over a private network. Data can be received in various forms including and ranging from receipt of a batch of data records to receipt of a single data record.
- the method 700 can also include receiving and storing data in a fraud detection system database.
- Data can be stored in various fashions. For example, received data can be stored in various records wherein a single data record corresponds to a single mortgage loan application. Data can also be stored using various key fields to distinguish data records from each other. As an example, data records provided by financial institutions (e.g., lenders in a mortgage transaction) may be stored using a key field identifier to distinguish data records between financial institutions. Such an identifier can be used to implement user pipeline control to one or more users for tracking their own data records.
- the method 700 can also include processing transaction information to determine a due diligence level associated with loan transactions. For example, a due diligence level associated with loan transactions. For example, a due diligence level associated with loan transactions. For example, a due diligence level associated with loan transactions. For example, a due diligence level associated with loan transactions. For example, a due diligence level associated with loan transactions. For example, a due diligence level associated with loan transactions. For example, a due diligence level associated with loan
- the method 700 may also include processing transaction information to determine potential fraudulent activity associated with loan transactions.
- a fraud detection system can process loan data transaction information for each borrower, applicant, lender professional, and/or company involved in a transaction.
- lenders can view information related to their mortgage applications via an internal pipeline analysis and also view information related to other lenders via an external pipeline analysis. Enabling lenders to view loan applications in their respective pipelines relative to loan applications in other lender pipelines advantageously enables lenders to determine if borrowers and/or brokers are applying or have applied to other lending institutions. Such information may be useful and desired by lenders in managing risk associated with mortgage loans.
- a fraud detection system can perform data validation checks. For example, a system can process received data to validate information related to the borrower's identity using an identity verification service. Validation can help determine if alternate information can be found associated with the borrower information. Such information can determine if a borrower has various name aliases as well as social security numbers and names.
- a fraud detection database can cross reference a social security number database to determine if a received name matches the database and also to determine if a received social security number is a valid number (e.g., the social security number is not on a death list).
- the method 700 can include processing information to determine compliance with existing laws and regulations. For example, in a mortgage transaction, lending institutions must comply with federal laws and regulations specific to consumer information and the establishment of relationships with their customers. These regulations are sometimes referred to as "Know Your Customer” regulations. Regulatory requirements associated with USA Patriot Act, Red Flag Rules (effective November 2008), and
- the method 700 can include processing information to determine background information for involved entities.
- This feature of embodiments of the invention advantageously enables review of loan transaction data related to third parties, industry professionals, and companies facilitating a loan transaction. Indeed, some embodiments can access one or more other databases (e.g., a proprietary and contributory database) that contain containing information related to professional licensing, publicly sourced derogatory incident reports, sanction and administrative action reports, and past or ongoing incidents of alleged fraud and misrepresentation.
- databases e.g., a proprietary and contributory database
- fraud detection systems can obtain information to verify a professional's credentials and risk associations by directly searching the contributory database for license validation and status, as well as any inclusion in a reported incident directly, or indirectly.
- the fraud detection system can provide results to users (as discussed below in more detail). For example, the system can return an "OK" or "ALERT" status based on received background information.
- embodiments of the present invention can uncovers relationships among transaction parties, identify conflicts in professional credentials, and enable greater visibility to prior adverse business activities or associations that may harm lenders.
- the method 700 can also include providing information
- Formats include, but are not limited to, email alerts and web-page listings. Formats can also include various reports provided by email and or return web-page listings.
- exemplary report formats can include line item results as a summary of loan transaction data that includes results from the processing, matching, and scoring algorithms.
- the system can enable a user to select specific fields with toggle functionality to present more detailed information about the loan transaction data and the processing results. The can also enable interactive user functionality to present loan transaction data in configurable formats through filtering, searching, and sorting capabilities. Still yet, the system can display a report with detailed validation, verification, and credentialing processing results related to the borrower/applicant and transaction professionals and/or companies.
- Other reporting capabilities include providing a due diligence level associated with a loan transaction and also an identification score.
- the due diligence level can be based partially on an aggregate view of all data and if certain data inconsistencies appear between reviewed data fields a returned due diligence level can be set to "HIGH RISK” or a "RED ALERT" status.
- Other due diligence levels can include "LOW RISK” or "MEDIUM RISK levels.
- returned results can include an identification score providing score information indicative of how a borrower's name checked with other checks. For example, if one database contains a name with a different SSN than submitted name, a borrower name identification score may be low indicating that the name issue should be further investigated. In another example, if one or more other database checks are performed and a borrower's name information is consistent among several records a high identification score can be returned indicating that a borrower's name is consistent with other data records.
- FIG. 8 illustrates a method 800 of an incident submission process according to some fraud detection embodiments of the present invention.
- method 800 can be performed in various orders (including differently than illustrated in FIG. 8), additional actions can be implemented as part of a method embodiment, and that some actions pictured in FIG. 8 are not necessary.
- FIG. 8 may be discussed herein as including certain other actions, these certain other actions may be carried out in various orders and/or as parts of the
- the method 800 can initiate at 805 by providing an interface enabling submission of fraud related incidents. Indeed, users of a fraud detection system according to some embodiments of the present invention can submit information related to fraud for further investigation and possible inclusion in the fraud detection system's database.
- the interface can be a web-based browser form capable of accepting user data entry related reporting a fraud related incident.
- data forms can be transmitted (e.g., email, FTP, or SFTP) to a fraud detection system for analysis and review.
- the method 800 can continue at 810 by populating data into an incident report submission form. Indeed in some embodiments, the system will automatically load data elements from loan transaction data into specified fields.
- the method 800 can continue at 815 by receiving incident report submission data.
- embodiments of a fraud detection system can provide the ability to query multiple databases. This enables users to determine whether those suspected of fraud may have been involved in other reported incidents.
- the databases include real estate and professional license data directly from the Mortgage Industry Data Exchange (“MIDEX").
- MIDEX is a repository containing industry-contributed incidents of fraud and material misrepresentation; publicly sourced information specific to administrative and disciplinary actions and sanctions; and professional license information.
- a fraud detection system enables users to query databases in an effort to verify and adjudicate the professionals of record associated with the incident.
- the method 800 can continue at 820 by providing incident report data for review.
- incident report data for review.
- some embodiments will provide reported fraud incident information for entry into the MIDEX incident submission processing queue.
- the system can also associate a reported incident to loan transaction data by using a unique identifier that relates the two entities or the loan transaction data and the incident report.
- the fraud detection system supports submission of an incident related to loan transactions, transaction parties or information contained in an official suspicious activity report.
- the review process can include administrative
- the method 800 can also continue at 825 by receiving follow up information related to a reported incident.
- the system can support a request for information and challenge of an incident submission.
- the challenge process enables an industry professional and/or company to request incident information naming the requestor.
- the requestor can submit information to an administrator via the system for verification and for potential future interaction regarding the submitted incident.
- An administrator can query the database for any incident reports associated with the requestor.
- the requestor will receive the incident information using an approved delivery method.
- the requestor is given authorization to respond or rebut information contained in the incident.
- the response or rebuttal is delivered to the incident submitter for review and response.
- An administrator acts as the intermediary between both parties during the rebuttal process.
- the method 800 can also continue at 830 by providing finalized incident report data into a fraud detection system database for review.
- the system can support database modifications to the incident report by an administrator. Data modification can include removal of incident text or the addition of a requestor's response to the original incident of record.
- the system can notify submitting entities with a relationship to the requestor as well as other user of interest with incident activity.
- the system can also notify all entities with an interest in the named professionals of any new incident activity or updates to an existing incident report.
- FIG. 9 illustrates a method 900 of authenticating and verifying entities and professionals involved in a financial application process according to some fraud detection embodiments of the present invention.
- method 900 can be performed in various orders (including differently than illustrated in FIG. 9), additional actions can be implemented as part of a method embodiment, and that some actions pictured in
- the method 900 can initiate by receiving names of entities involved in a loan transaction. Entities can include people, names of businesses, and companies. People may include borrowers, lenders, brokers, and realtors. By receiving this information, fraud detection systems according to the present invention can perform background and credential checks on the received information. Indeed, at 910, the method 900 may include comparing received information with records already existing in the database. The result of such checks can provide information beneficial to users thereby enabling users to obtain background information on those involved in a loan transaction.
- the method 900 can also include obtaining information from other existing databases at 915.
- These databases can include private and publicly available databases.
- the existing databases can be managed by third parties and they databases can be located distant from a fraud detection system.
- the existing databases can, for example, be managed by governmental or industry licensing groups and contain entity licensing and/or sanction information. Such features enable users to query a database for authorized and credentialed professionals when a loan transaction is commenced.
- a fraud detection system enabling such entity checking can also facilitate queries to credentialed professionals list owned by the user of the system.
- fraud detection systems will upload information about a individual professional, as it is presented to the system user.
- the system can then engage a workflow of data service calls and return specific information related to, for example, professional license status, incidents, OFAC compliance, criminal background, and a synthetic identity check.
- specific information for example, professional license status, incidents, OFAC compliance, criminal background, and a synthetic identity check.
- users and/or a fraud detection system can compare the received background information to received loan transaction information to perform background checks at 920.
- the method 900 can also include reporting entity information to users at 925.
- fraud detection system embodiments can be configured to present a return list to users for selection when prompted to input information about a loan transaction professional.
- the process for professional credentialing may be a workflow of choreographed data services that
- Embodiments can also include providing a credentialed professional list with access to system detail reports and an exception list that contains professionals and/or companies that have been identified as not credentialed to do business. Additional embodiments can also enable uploads of professionals not credentialed to do business with the system user, by the system user.
- embodiments of the present invention can be used to identify a potential fraud scheme.
- the identification can be determined by analyzing results of data field analysis and comparison. For example, the inventors have discovered that by analyzing matching characteristics of certain data fields (e.g., data fields have data that matches or does not match), one or more potential fraud schemes can be provided to a user. Matching 5 characteristics can also include analyzing the "N" (no match) and "E" (exact match) processes discussed above.
- the below provided table (TABLE I) provides a series of test scenarios for implementing a fraud scheme analysis process in accordance with some embodiments.
- embodiments of the present invention can be configured to review the results of a matching analysis for various data fields. Based on the analysis, a potential fraud scheme can be provided to a user. For example, if a no match result is returned for loan borrower name, appraisal value, application data, and loan seller name in concert with a match for originator company and name and appraiser name information, this information can indicate a possible flipping fraud scheme. As a result, embodiments of the present invention can return this information to a user so that a user can further investigate.
- Shot-Gunning, double escrow, churning, chunking, and straw-buyer alerts can be similarly returned to users if a matching analysis is in line with the above table entries.
- possible fraud scenarios can be returned for both a user's internal pipeline and also for records external to a user's pipeline.
- a processing module can be configured to separate loan transaction information for review by users into multiple pipelines using an identifier configured to distinguish users.
- a processing module can be configured to separate loan transaction information into an internal pipeline comprising loan information associated with a first lender and an external pipeline containing loan information associated with lenders other than the first lender.
- a processing module can be configured to associate at least one of a due diligence level and identification score with one or more data records.
- a processing module can be configured to determine if one or more data records complies with financial laws and regulations.
- a processing module can be configured to maintain one or more data records in an active pipeline for a predetermined amount of time, the one or more data records in the active pipeline being associated with a single lender.
- yet another aspect can include providing users information about one or more data records relating to mortgage applications at predetermined stages of the mortgage applications. Also, in accordance with
- data records can have a uniform format such that the processing module can compared a data record to another data record to determine consistencies and inconsistencies.
- a method can include receiving fraud incident data information and storing such information as a data record.
- a fraud detection method can also include receiving a query based on or more data fields and to return data records responsive to the query.
- a processing module can be configured to monitor one or more data records for a predetermined period of time and provide reports detailing any changes in the one or more data records.
- a processing module can be configured to provide reports comprising information about a mortgage application proximate at least one of a pre-funding application stage, a loan funding stage, an investor servicing stage, and a loan servicing stage.
- Embodiments of the present invention can still have other features. For example, some embodiments can be configured to validate the one or more data records to ensure that the one or more data records comprise data based on a predetermined data format. Some embodiments can also be configured to receive updated data pertaining to one or more data records and providing administrative operating reports comprising operational information. Such operational information can include database operational status, user interaction information, user submitted information, and other administrative data functions as desired. As discussed herein, embodiments of the present invention can receive information from unique users, which can comprise lenders, brokers, and borrowers. Embodiments can also include at least one webserver operatively configured to provide data interfaces and/or internet portals for use in communication networks. Also, embodiments of the present invention enable users to customize reports provided to them. For example, a reporting module can be operatively configured to receive report format information from a user and to provide report information to a user based at least partially on the received report format information.
Abstract
Description
Claims
Priority Applications (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
GB1012091A GB2469948A (en) | 2007-12-20 | 2008-12-19 | Mortgage fraud detection systems and methods |
CA2710466A CA2710466A1 (en) | 2007-12-20 | 2008-12-19 | Mortgage fraud detection systems and methods |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US1542907P | 2007-12-20 | 2007-12-20 | |
US61/015,429 | 2007-12-20 |
Publications (2)
Publication Number | Publication Date |
---|---|
WO2009086143A2 true WO2009086143A2 (en) | 2009-07-09 |
WO2009086143A3 WO2009086143A3 (en) | 2009-10-08 |
Family
ID=40688786
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/US2008/087745 WO2009086143A2 (en) | 2007-12-20 | 2008-12-19 | Mortgage fraud detection systems and methods |
Country Status (4)
Country | Link |
---|---|
US (2) | US7546271B1 (en) |
CA (1) | CA2710466A1 (en) |
GB (1) | GB2469948A (en) |
WO (1) | WO2009086143A2 (en) |
Families Citing this family (99)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US9710852B1 (en) | 2002-05-30 | 2017-07-18 | Consumerinfo.Com, Inc. | Credit report timeline user interface |
US9400589B1 (en) | 2002-05-30 | 2016-07-26 | Consumerinfo.Com, Inc. | Circular rotational interface for display of consumer credit information |
US8682755B2 (en) * | 2012-07-03 | 2014-03-25 | Lexisnexis Risk Solutions Fl Inc. | Systems and methods for detecting tax refund fraud |
US8688507B2 (en) * | 2005-03-21 | 2014-04-01 | Oversight Technologies, Inc. | Methods and systems for monitoring transaction entity versions for policy compliance |
US20060271454A1 (en) * | 2005-05-25 | 2006-11-30 | Strom Steven R | Method of analyzing a sale process for a company |
US8635138B2 (en) | 2009-07-16 | 2014-01-21 | Steven R. Strom | Method of analyzing a sale process for an entity |
US8285656B1 (en) | 2007-03-30 | 2012-10-09 | Consumerinfo.Com, Inc. | Systems and methods for data verification |
US20140244510A1 (en) * | 2007-05-23 | 2014-08-28 | Raymond de Beasley | Privacy protection system and method |
WO2008147918A2 (en) | 2007-05-25 | 2008-12-04 | Experian Information Solutions, Inc. | System and method for automated detection of never-pay data sets |
US20090204521A1 (en) * | 2007-12-13 | 2009-08-13 | De Sena Francis E | Method of and system for web-based managing and reporting mortgage transactions |
US8127986B1 (en) | 2007-12-14 | 2012-03-06 | Consumerinfo.Com, Inc. | Card registry systems and methods |
US9990674B1 (en) | 2007-12-14 | 2018-06-05 | Consumerinfo.Com, Inc. | Card registry systems and methods |
US7546271B1 (en) * | 2007-12-20 | 2009-06-09 | Choicepoint Asset Company | Mortgage fraud detection systems and methods |
US8312033B1 (en) | 2008-06-26 | 2012-11-13 | Experian Marketing Solutions, Inc. | Systems and methods for providing an integrated identifier |
US9256904B1 (en) | 2008-08-14 | 2016-02-09 | Experian Information Solutions, Inc. | Multi-bureau credit file freeze and unfreeze |
US8060424B2 (en) | 2008-11-05 | 2011-11-15 | Consumerinfo.Com, Inc. | On-line method and system for monitoring and reporting unused available credit |
US8380569B2 (en) | 2009-04-16 | 2013-02-19 | Visa International Service Association, Inc. | Method and system for advanced warning alerts using advanced identification system for identifying fraud detection and reporting |
US8700522B2 (en) * | 2009-10-30 | 2014-04-15 | Accenture Global Services Limited | Loan portfolio management tool |
US20110238566A1 (en) * | 2010-02-16 | 2011-09-29 | Digital Risk, Llc | System and methods for determining and reporting risk associated with financial instruments |
US9652802B1 (en) | 2010-03-24 | 2017-05-16 | Consumerinfo.Com, Inc. | Indirect monitoring and reporting of a user's credit data |
US8725613B1 (en) * | 2010-04-27 | 2014-05-13 | Experian Information Solutions, Inc. | Systems and methods for early account score and notification |
US8515863B1 (en) | 2010-09-01 | 2013-08-20 | Federal Home Loan Mortgage Corporation | Systems and methods for measuring data quality over time |
US8498929B2 (en) * | 2010-09-15 | 2013-07-30 | Corelogic Solutions, Llc | System, method and computer program storage device for detecting short sale fraud |
US8930262B1 (en) | 2010-11-02 | 2015-01-06 | Experian Technology Ltd. | Systems and methods of assisted strategy design |
US9147042B1 (en) * | 2010-11-22 | 2015-09-29 | Experian Information Solutions, Inc. | Systems and methods for data verification |
US20120143649A1 (en) * | 2010-12-01 | 2012-06-07 | 9133 1280 Quebec Inc. | Method and system for dynamically detecting illegal activity |
US9349115B2 (en) * | 2011-01-11 | 2016-05-24 | International Business Machines Corporation | Data management and control using data importance levels |
EP2676197B1 (en) | 2011-02-18 | 2018-11-28 | CSidentity Corporation | System and methods for identifying compromised personally identifiable information on the internet |
US20120215658A1 (en) * | 2011-02-23 | 2012-08-23 | dBay Inc. | Pin-based payment confirmation |
US9558519B1 (en) | 2011-04-29 | 2017-01-31 | Consumerinfo.Com, Inc. | Exposing reporting cycle information |
US9665854B1 (en) | 2011-06-16 | 2017-05-30 | Consumerinfo.Com, Inc. | Authentication alerts |
US20120330819A1 (en) * | 2011-06-21 | 2012-12-27 | Early Warning Services, Llc | System and method for locating and accessing account data |
US10607284B2 (en) | 2011-06-21 | 2020-03-31 | Early Warning Services, Llc | System and method to search and verify borrower information using banking and investment account data and process to systematically share information with lenders and government sponsored agencies for underwriting and securitization phases of the lending cycle |
US8396877B2 (en) * | 2011-06-27 | 2013-03-12 | Raytheon Company | Method and apparatus for generating a fused view of one or more people |
US9483606B1 (en) | 2011-07-08 | 2016-11-01 | Consumerinfo.Com, Inc. | Lifescore |
US9106691B1 (en) | 2011-09-16 | 2015-08-11 | Consumerinfo.Com, Inc. | Systems and methods of identity protection and management |
US8738516B1 (en) | 2011-10-13 | 2014-05-27 | Consumerinfo.Com, Inc. | Debt services candidate locator |
US11030562B1 (en) | 2011-10-31 | 2021-06-08 | Consumerinfo.Com, Inc. | Pre-data breach monitoring |
US9361656B2 (en) | 2012-01-09 | 2016-06-07 | W. C. Taylor, III | Data mining and logic checking tools |
US9853959B1 (en) | 2012-05-07 | 2017-12-26 | Consumerinfo.Com, Inc. | Storage and maintenance of personal data |
US20130332374A1 (en) * | 2012-06-09 | 2013-12-12 | Scott Hartnett | Fraud prevention for real estate transactions |
US8918891B2 (en) * | 2012-06-12 | 2014-12-23 | Id Analytics, Inc. | Identity manipulation detection system and method |
US10043213B2 (en) * | 2012-07-03 | 2018-08-07 | Lexisnexis Risk Solutions Fl Inc. | Systems and methods for improving computation efficiency in the detection of fraud indicators for loans with multiple applicants |
US10089686B2 (en) | 2012-07-03 | 2018-10-02 | Lexisnexis Risk Solutions Fl Inc. | Systems and methods for increasing efficiency in the detection of identity-based fraud indicators |
US9654541B1 (en) | 2012-11-12 | 2017-05-16 | Consumerinfo.Com, Inc. | Aggregating user web browsing data |
US9117186B2 (en) * | 2012-11-21 | 2015-08-25 | Cellco Partnership | Joint marketed customer hub |
US9916621B1 (en) | 2012-11-30 | 2018-03-13 | Consumerinfo.Com, Inc. | Presentation of credit score factors |
US10255598B1 (en) | 2012-12-06 | 2019-04-09 | Consumerinfo.Com, Inc. | Credit card account data extraction |
US9697263B1 (en) | 2013-03-04 | 2017-07-04 | Experian Information Solutions, Inc. | Consumer data request fulfillment system |
US9406085B1 (en) | 2013-03-14 | 2016-08-02 | Consumerinfo.Com, Inc. | System and methods for credit dispute processing, resolution, and reporting |
US9870589B1 (en) | 2013-03-14 | 2018-01-16 | Consumerinfo.Com, Inc. | Credit utilization tracking and reporting |
US10102570B1 (en) | 2013-03-14 | 2018-10-16 | Consumerinfo.Com, Inc. | Account vulnerability alerts |
US20140279380A1 (en) * | 2013-03-14 | 2014-09-18 | Fannie Mae | Automated searching credit reports to identify potential defaulters |
US8812387B1 (en) | 2013-03-14 | 2014-08-19 | Csidentity Corporation | System and method for identifying related credit inquiries |
US10664936B2 (en) | 2013-03-15 | 2020-05-26 | Csidentity Corporation | Authentication systems and methods for on-demand products |
US9633322B1 (en) | 2013-03-15 | 2017-04-25 | Consumerinfo.Com, Inc. | Adjustment of knowledge-based authentication |
US9230101B2 (en) * | 2013-03-15 | 2016-01-05 | Pinkerton Consulting And Investigations, Inc. | Providing alerts based on unstructured information methods and apparatus |
US10685398B1 (en) | 2013-04-23 | 2020-06-16 | Consumerinfo.Com, Inc. | Presenting credit score information |
US9721147B1 (en) | 2013-05-23 | 2017-08-01 | Consumerinfo.Com, Inc. | Digital identity |
US20160328794A1 (en) * | 2013-07-24 | 2016-11-10 | Pitch Point Solutions, Inc. | Methods and systems for improved application submissions |
US9443268B1 (en) | 2013-08-16 | 2016-09-13 | Consumerinfo.Com, Inc. | Bill payment and reporting |
US20150066738A1 (en) * | 2013-08-30 | 2015-03-05 | Corelogic Solutions, Llc | System amd method for detecting short sale fraud |
US10410282B2 (en) * | 2013-09-12 | 2019-09-10 | Capital One Services, Llc | Systems and methods for a refinancing savings widget |
US10102536B1 (en) | 2013-11-15 | 2018-10-16 | Experian Information Solutions, Inc. | Micro-geographic aggregation system |
US10325314B1 (en) | 2013-11-15 | 2019-06-18 | Consumerinfo.Com, Inc. | Payment reporting systems |
US9477737B1 (en) | 2013-11-20 | 2016-10-25 | Consumerinfo.Com, Inc. | Systems and user interfaces for dynamic access of multiple remote databases and synchronization of data based on user rules |
US20150142629A1 (en) * | 2013-11-20 | 2015-05-21 | Bank Of America Corporation | Detecting unusual activity in cash vault transactions |
US9529851B1 (en) | 2013-12-02 | 2016-12-27 | Experian Information Solutions, Inc. | Server architecture for electronic data quality processing |
US10262362B1 (en) | 2014-02-14 | 2019-04-16 | Experian Information Solutions, Inc. | Automatic generation of code for attributes |
US9009827B1 (en) | 2014-02-20 | 2015-04-14 | Palantir Technologies Inc. | Security sharing system |
US20150235334A1 (en) * | 2014-02-20 | 2015-08-20 | Palantir Technologies Inc. | Healthcare fraud sharing system |
USD759690S1 (en) | 2014-03-25 | 2016-06-21 | Consumerinfo.Com, Inc. | Display screen or portion thereof with graphical user interface |
USD760256S1 (en) | 2014-03-25 | 2016-06-28 | Consumerinfo.Com, Inc. | Display screen or portion thereof with graphical user interface |
USD759689S1 (en) | 2014-03-25 | 2016-06-21 | Consumerinfo.Com, Inc. | Display screen or portion thereof with graphical user interface |
US9892457B1 (en) | 2014-04-16 | 2018-02-13 | Consumerinfo.Com, Inc. | Providing credit data in search results |
US10373240B1 (en) | 2014-04-25 | 2019-08-06 | Csidentity Corporation | Systems, methods and computer-program products for eligibility verification |
US9509705B2 (en) * | 2014-08-07 | 2016-11-29 | Wells Fargo Bank, N.A. | Automated secondary linking for fraud detection systems |
US10339527B1 (en) | 2014-10-31 | 2019-07-02 | Experian Information Solutions, Inc. | System and architecture for electronic fraud detection |
US20160171564A1 (en) * | 2014-12-11 | 2016-06-16 | Fannie Mae | Subject appraisal discrepancy analysis |
US11263600B2 (en) | 2015-03-24 | 2022-03-01 | 4 S Technologies, LLC | Automated trustee payments system |
US11151468B1 (en) | 2015-07-02 | 2021-10-19 | Experian Information Solutions, Inc. | Behavior analysis using distributed representations of event data |
US10757154B1 (en) | 2015-11-24 | 2020-08-25 | Experian Information Solutions, Inc. | Real-time event-based notification system |
US11227001B2 (en) | 2017-01-31 | 2022-01-18 | Experian Information Solutions, Inc. | Massive scale heterogeneous data ingestion and user resolution |
US10735183B1 (en) | 2017-06-30 | 2020-08-04 | Experian Information Solutions, Inc. | Symmetric encryption for private smart contracts among multiple parties in a private peer-to-peer network |
US10699028B1 (en) | 2017-09-28 | 2020-06-30 | Csidentity Corporation | Identity security architecture systems and methods |
US10896472B1 (en) | 2017-11-14 | 2021-01-19 | Csidentity Corporation | Security and identity verification system and architecture |
US20190333175A1 (en) * | 2018-04-30 | 2019-10-31 | Deckard Technologies, Inc. | Detecting and validating real estate transfer events through data mining, natural language processing, and machine learning |
US10911234B2 (en) | 2018-06-22 | 2021-02-02 | Experian Information Solutions, Inc. | System and method for a token gateway environment |
US20200074541A1 (en) | 2018-09-05 | 2020-03-05 | Consumerinfo.Com, Inc. | Generation of data structures based on categories of matched data items |
US10963434B1 (en) | 2018-09-07 | 2021-03-30 | Experian Information Solutions, Inc. | Data architecture for supporting multiple search models |
US11315179B1 (en) | 2018-11-16 | 2022-04-26 | Consumerinfo.Com, Inc. | Methods and apparatuses for customized card recommendations |
US11178179B2 (en) | 2018-12-10 | 2021-11-16 | Capital One Services, Llc | Synthetic identity signal network |
US11816747B1 (en) * | 2018-12-12 | 2023-11-14 | United Services Automobile Association (Usaa) | Systems and methods for mining data for property usage |
WO2020146667A1 (en) | 2019-01-11 | 2020-07-16 | Experian Information Solutions, Inc. | Systems and methods for secure data aggregation and computation |
US11238656B1 (en) | 2019-02-22 | 2022-02-01 | Consumerinfo.Com, Inc. | System and method for an augmented reality experience via an artificial intelligence bot |
CN111429268B (en) * | 2020-03-30 | 2023-11-24 | 上海德易车信息科技有限公司 | Vehicle credit risk detection method, terminal equipment and storage medium |
US20220207630A1 (en) * | 2020-12-29 | 2022-06-30 | Toby Michael Cohen | System and method for authorizing transfer requests of physical locations |
US11880377B1 (en) | 2021-03-26 | 2024-01-23 | Experian Information Solutions, Inc. | Systems and methods for entity resolution |
CN114581219A (en) * | 2022-04-29 | 2022-06-03 | 弘沣智安科技(北京)有限公司 | Anti-telecommunication network fraud early warning method and system |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20020133371A1 (en) * | 2001-01-24 | 2002-09-19 | Cole James A. | Automated mortgage fraud prevention method and system |
US20070073519A1 (en) * | 2005-05-31 | 2007-03-29 | Long Kurt J | System and Method of Fraud and Misuse Detection Using Event Logs |
US20070219819A1 (en) * | 2006-03-14 | 2007-09-20 | Title Insurance National Information Exchange Llc | Method and system for detecting title fraud |
US20070226129A1 (en) * | 2006-03-24 | 2007-09-27 | Yuansong Liao | System and method of detecting mortgage related fraud |
Family Cites Families (39)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6029154A (en) * | 1997-07-28 | 2000-02-22 | Internet Commerce Services Corporation | Method and system for detecting fraud in a credit card transaction over the internet |
US6581039B2 (en) * | 1999-11-23 | 2003-06-17 | Accenture Llp | Report searching in a merger and acquisition environment |
US6418436B1 (en) * | 1999-12-20 | 2002-07-09 | First Data Corporation | Scoring methodology for purchasing card fraud detection |
US20030097342A1 (en) * | 2000-01-24 | 2003-05-22 | Whittingtom Barry R. | Method for verifying employment data |
US6871140B1 (en) * | 2000-02-25 | 2005-03-22 | Costar Group, Inc. | System and method for collection, distribution, and use of information in connection with commercial real estate |
US6766322B1 (en) * | 2000-06-23 | 2004-07-20 | G. Randall Bell | Real estate disclosure reporting method |
US20030009418A1 (en) * | 2000-12-08 | 2003-01-09 | Green Gerald M. | Systems and methods for electronically verifying and processing information |
US7366694B2 (en) * | 2001-08-16 | 2008-04-29 | Mortgage Grader, Inc. | Credit/financing process |
US7689503B2 (en) * | 2001-11-13 | 2010-03-30 | Interthinx, Inc. | Predatory lending detection system and method therefor |
US8458082B2 (en) * | 2001-11-13 | 2013-06-04 | Interthinx, Inc. | Automated loan risk assessment system and method |
US20030182151A1 (en) * | 2002-02-26 | 2003-09-25 | Neal Taslitz | Method of using biometric measurements as a legal seal for authenticating real estate deeds and mortgages |
US20030187783A1 (en) * | 2002-03-27 | 2003-10-02 | First Data Corporation | Systems and methods to monitor credit fraud |
US20040030649A1 (en) * | 2002-05-06 | 2004-02-12 | Chris Nelson | System and method of application processing |
US7472089B2 (en) * | 2002-08-15 | 2008-12-30 | Ellie Mae, Inc. | Loan origination system interface for online loan application processing |
US20040064401A1 (en) * | 2002-09-27 | 2004-04-01 | Capital One Financial Corporation | Systems and methods for detecting fraudulent information |
US6715672B1 (en) * | 2002-10-23 | 2004-04-06 | Donald Tetro | System and method for enhanced fraud detection in automated electronic credit card processing |
US20040138912A1 (en) * | 2002-11-04 | 2004-07-15 | Loan Recapture Services, Llc | Multiple listing services (MLS) data redistribution |
US20050154665A1 (en) | 2002-11-22 | 2005-07-14 | Florida Bankers Association, Inc. | Fraud prevention system |
US7458508B1 (en) * | 2003-05-12 | 2008-12-02 | Id Analytics, Inc. | System and method for identity-based fraud detection |
US7373669B2 (en) * | 2003-08-13 | 2008-05-13 | The 41St Parameter, Inc. | Method and system for determining presence of probable error or fraud in a data set by linking common data values or elements |
US7599882B2 (en) | 2003-11-14 | 2009-10-06 | First American Corelogic, Inc. | Method for mortgage fraud detection |
US20050187863A1 (en) | 2004-02-20 | 2005-08-25 | Whinery Christopher S. | Method and system for protecting real estate from fraudulent transactions |
US8055518B2 (en) * | 2004-03-15 | 2011-11-08 | Arthur J Prieston | Method for handling claims arising under representation and warranty insurance for mortgage loans |
US7311248B1 (en) * | 2004-08-12 | 2007-12-25 | Prairie Systems, Inc. | Method and system for automatically detecting fraudulent applications |
NZ554421A (en) | 2004-10-13 | 2010-09-30 | Ares Capital Man Pty Ltd | Data processing system supporting decisions to accept or reject applications for financial accommodation |
CA2524227A1 (en) * | 2004-10-22 | 2006-04-22 | The First American Corporation | Product, system and method for certification of closing and mortgage loan fulfillment |
US20060218079A1 (en) * | 2005-02-08 | 2006-09-28 | Goldblatt Joel N | Web-based consumer loan database with automated controls for preventing predatory lending practices |
US20060224499A1 (en) | 2005-03-29 | 2006-10-05 | First American Real Estate Solutions, L.P. | Method and apparatus for computing a loan quality score |
US20070174214A1 (en) * | 2005-04-13 | 2007-07-26 | Robert Welsh | Integrated fraud management systems and methods |
US20080021801A1 (en) * | 2005-05-31 | 2008-01-24 | Yuh-Shen Song | Dynamic multidimensional risk-weighted suspicious activities detector |
US20070016522A1 (en) | 2005-07-15 | 2007-01-18 | Zhiping Wang | Data processing system for a billing address-based credit watch |
WO2007028048A2 (en) * | 2005-09-02 | 2007-03-08 | Fair Isaac Corporation | Systems and methods for detecting fraud |
US20070067234A1 (en) | 2005-09-21 | 2007-03-22 | Beech James J | Mortgage loan system and method |
US20070208641A1 (en) * | 2006-03-03 | 2007-09-06 | Bayview Lending Group Llc | Automated loan processing system and method for processing loans |
WO2007120844A2 (en) * | 2006-04-12 | 2007-10-25 | Chimento Marc A | System and method for screening for fraud in commercial transactions |
US20080109349A1 (en) * | 2006-11-08 | 2008-05-08 | George Jenich | System and method of processing loan applications |
US8612320B2 (en) * | 2006-12-14 | 2013-12-17 | Corelogic Solutions, Llc | Method and apparatus for detecting fraudulent loans |
US20080167883A1 (en) * | 2007-01-05 | 2008-07-10 | Ramin Thavildar Khazaneh | Method and System for Monitoring and Protecting Real Estate Title (Ownership) Against Fraudulent Transaction (Title Theft) and Mortgage Fraud |
US7546271B1 (en) * | 2007-12-20 | 2009-06-09 | Choicepoint Asset Company | Mortgage fraud detection systems and methods |
-
2008
- 2008-07-16 US US12/174,591 patent/US7546271B1/en not_active Expired - Fee Related
- 2008-12-19 GB GB1012091A patent/GB2469948A/en not_active Withdrawn
- 2008-12-19 WO PCT/US2008/087745 patent/WO2009086143A2/en active Application Filing
- 2008-12-19 CA CA2710466A patent/CA2710466A1/en not_active Abandoned
-
2009
- 2009-06-09 US US12/481,546 patent/US20100241558A1/en not_active Abandoned
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20020133371A1 (en) * | 2001-01-24 | 2002-09-19 | Cole James A. | Automated mortgage fraud prevention method and system |
US20070073519A1 (en) * | 2005-05-31 | 2007-03-29 | Long Kurt J | System and Method of Fraud and Misuse Detection Using Event Logs |
US20070219819A1 (en) * | 2006-03-14 | 2007-09-20 | Title Insurance National Information Exchange Llc | Method and system for detecting title fraud |
US20070226129A1 (en) * | 2006-03-24 | 2007-09-27 | Yuansong Liao | System and method of detecting mortgage related fraud |
Also Published As
Publication number | Publication date |
---|---|
US20090164232A1 (en) | 2009-06-25 |
US20100241558A1 (en) | 2010-09-23 |
WO2009086143A3 (en) | 2009-10-08 |
GB2469948A (en) | 2010-11-03 |
US7546271B1 (en) | 2009-06-09 |
GB201012091D0 (en) | 2010-09-01 |
CA2710466A1 (en) | 2009-07-09 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US7546271B1 (en) | Mortgage fraud detection systems and methods | |
JP7350819B2 (en) | Transaction monitoring system | |
US10657590B2 (en) | System and method for an electronic lending system | |
US8732084B2 (en) | Identification and risk evaluation | |
US8468088B2 (en) | Automated mining and processing of data associated with real estate | |
US8781956B2 (en) | Systems and methods for making structured reference credit decisions | |
US20020133371A1 (en) | Automated mortgage fraud prevention method and system | |
US20140058910A1 (en) | Method for detecting identity misrepresentation in fraudulent tax returns | |
US20160196605A1 (en) | System And Method To Search And Verify Borrower Information Using Banking And Investment Account Data And Process To Systematically Share Information With Lenders and Government Sponsored Agencies For Underwriting And Securitization Phases Of The Lending Cycle | |
JP2022520824A (en) | Intelligent warning system | |
JP2005503597A (en) | Automated political risk management | |
US20070219819A1 (en) | Method and system for detecting title fraud | |
US8489500B2 (en) | Method and system for compliance hosting | |
US20100070407A1 (en) | System and method for on-line lending with early warning system | |
WO2000021011A9 (en) | System and method for processing loans | |
US20140279386A1 (en) | Methods and system for mining and analyzing real estate information | |
KR20090001940A (en) | System and method for preliminarily selecting insolvent credit transaction company and program recording medium | |
US8117101B1 (en) | Database structure for a consumer reporting agency | |
von Lampe | Mortgage fraud and organized crime in Canada: strategic intelligence brief | |
Yezer | Personal Privacy of HMDA in a World of Big Data | |
Booth | Public record and other information on hidden assets | |
Network | Mortgage Loan Fraud:. | |
Parker | Problem Bank Resolution and Supervision | |
ES | Mortgage Loan Fraud Connections with Other Financial Crime | |
ENFORCEMENT | INTRODUCTION TO THE BANK SECRECY ACT |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 08867549 Country of ref document: EP Kind code of ref document: A2 |
|
WWE | Wipo information: entry into national phase |
Ref document number: 2710466 Country of ref document: CA |
|
NENP | Non-entry into the national phase |
Ref country code: DE |
|
WWE | Wipo information: entry into national phase |
Ref document number: 4640/DELNP/2010 Country of ref document: IN |
|
ENP | Entry into the national phase |
Ref document number: 1012091 Country of ref document: GB Kind code of ref document: A Free format text: PCT FILING DATE = 20081219 |
|
WWE | Wipo information: entry into national phase |
Ref document number: 1012091.3 Country of ref document: GB |
|
122 | Ep: pct application non-entry in european phase |
Ref document number: 08867549 Country of ref document: EP Kind code of ref document: A2 |