Secure data processing system, such as a system for detecting fraud and expediting note processing

• US 20060202012A1
• Filed: 10/24/2005
• Published: 09/14/2006
• Est. Priority Date: 11/12/2004
• Status: Abandoned Application

• Current Assignee: VERO
• Original Assignee: VERO
• Inventors: Buhler, Matthew E., Ford, J. Milford, Grano, David, Lawson, Karen
• Application Number:
  US11/258,407
• Publication Number:
  US 20060202012A1
• Time in Patent Office: Days
• US Class Current: 235/379
• CPC Class Codes: G06Q 20/04 Payment circuits
• CPC Class Codes: G06Q 20/042 characterized in that the payment protocol involves at least one cheque
• CPC Class Codes: G06Q 20/10 specially adapted for electronic funds transfer [EFT] systems; specially adapted for home banking systems
• CPC Class Codes: G06Q 20/26 Debit schemes, e.g. "pay now"
• CPC Class Codes: G06Q 20/40 Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
• CPC Class Codes: G06Q 20/4016 involving fraud or risk level assessment in transaction processing
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G07F 19/20 Automatic teller machines [ATMs]
• CPC Class Codes: G07F 19/207 Surveillance aspects at ATMs

Abstract

A transaction fraud detection system allows for the processing of documents such as checks in a way that reduces the possibility of fraud. The transaction fraud detection system provides customer enrollment and check enrollment functionality. It applies rules to data collected using the customer enrollment, the check enrollment, and possibly other data sources to determine a risk associated with a transaction, which may include generating a score. The applied rules may be customized via risk modeling functionality. For example, an administrator may be able to custom-select the types of rules to apply, the weighting or value given to any given rule, etc. The risk modeling may be at least partially automated, and may involve self-learning aspects that allow the risk modeling and/or application of rules to be at least partially based on past transactions.

Claims

1. A method for check cashing that is at least partially implemented at a computer, the method comprising:

enrolling a customer to associate the customer with a check cashing system, or, if the customer has been previously enrolled, verifying the identity of the previously enrolled customer;

enrolling a check that the customer wishes to cash, wherein the enrolling of the check includes;

capturing at least one image associated with the check,

isolating areas of the captured image for individual analysis,

if the check is from an unrecognized maker, creating a new processing profile based on the captured image, and

if the check is from a recognized maker, applying information from a previously generated processing template to the check;

analyzing the isolated areas based, at least in part, on the profile or through an applied template, wherein the analyzing includes;

applying rule-based algorithms to elements and characteristics of the isolated areas, and

generating at least one score for the enrolled check based on applying the rule-based algorithms, and

determining whether or not the enrolled check should be cashed based on the at least one score.

2. The method of claim 1 wherein the rule-based algorithms are generated by a method comprising:

decomposing a fraud risk from a singular objective to a set of optimized constraints; and

converting the set of optimized constraints to specific transaction-based rules through an automated process.

3. The method of claim 1 further comprising dispensing funds to the customer through a directed dispensing of cash, a partial allocation of funds to a virtual account, or a redirection of funds to another transaction type.

4. The method of claim 1 wherein enrolling the customer includes:

collecting data from the customer, including inputting biometric data collected from the customer in the form of fingerprint, retinal scan, voice pattern, or other personal identification information, taking a digital image of the customer'"'"'s face, and scanning and authenticating a government issued or other identification card presented by the customer;

automatically populating account setup data structures using the collected data; and

using an algorithm to uniquely encrypt the collected data so that the encrypted collected data is usable to identify the customer during subsequent transactions.

5. The method of claim 1 wherein verifying the identity of the customer includes reading an access card provided by the customer and receiving a personal identification number input from the customer.

6. The method of claim 1 wherein verifying the identity of the customer includes authenticating an enrollment attribute set provided by the customer upon request.

7. The method of claim 1 wherein determining whether or not the enrolled check should be cashed is an automated decision, based at least in part on a risk tolerance level specified by an administrative user and subsequently decomposed into multiple attributes or parameters that are used as input when applying the rule-based algorithms.

8. A method for customizable risk management in a check cashing system, the method comprising:

decomposing a fraud risk scenario associated with check cashing, wherein decomposing the fraud risk is performed using a first at least partially automated process for generating a set of risk factor objects related to the fraud risk scenario as applied to a specified market segment;

converting the set of software-implemented risk factor objects into a rule set for application in one or more check cashing transactions, wherein the converting is performed using a second at least partially automated process; and

applying the at least one rule set during an analysis used to determine whether a check should be cashed.

9. The method of claim 8 wherein the set of software-implemented risk factor objects includes risk factor objects from a group of risk factor objects consisting of:

object to be considered;

category of object to be considered;

target associated with object to be considered;

importance of object to be considered;

process step;

primary actor; and

validation.

10. The method of claim 8 wherein the specified market segment includes at least one market segment from a group of market segments consisting of:

bank;

casino;

grocery store; and

retail.

11. A check cashing system, embodied, at least in part, in a computer-readable medium, the system comprising:

a first set of data elements representing transaction metadata or informational components including information derived from and associated with one or more images associated with a check to be cashed in a check cashing transaction, wherein the check is associated with a check cashing customer and a check maker;

a second set of data elements derived from characteristics of the transaction including information retrieved from the one or more images, information associated with the check cashing transaction, or both information retrieved from the one or more images and information associated with the check cashing transaction;

a methodologies component for performing rules-based analysis on the first set of data elements and the second set of data elements to calculate at least one score associated with the check; and

an auto-decisioning component for providing an output indicating whether or not the check should be cashed, wherein the auto-decisioning component provides the output based, at least in part, on an aggregate score, and wherein the auto-decisioning is not based on whether the check cashing customer has used the system previously to cash a previous check for a similar amount.

12. The system of claim 11 wherein:

the first set of data elements includes a variable number of types derived information types associated with graphical elements taken from the one or more images; and

the second set of data elements includes information associated with characteristics gathered from any one or more of a customer enrollment process, a check enrollment process, a pending transaction, one or more third-party databases, a fraud history associated with the check cashing customer, and a fraud history associated with the check maker.

13. The system of claim 11 wherein the rules-based analysis by the methodologies component includes the application of one or more rules from a group of rules consisting of:

value rules;

derived rules; and

composite rules based on combinations of the value rules and the derived rules.

14. The system of claim 11 wherein the rules-based analysis by the methodologies component includes the application of one or more rules to variables from a group of variables consisting of:

account status, check velocity, check amount variance, check frequency variance, check date variance, check amount threshold, maker amount threshold, courtesy and legal amount, payee recognition, signature verification, maker validation, magnetic ink check resolution data, header verification, check and vendor number, enhanced image analysis results, ID card type, social security number, date of birth, permanent address, death master list, third-party database variables, composite score, data range, account data freshness, customer level, and card activity.

15. The system of claim 11 wherein the rules-based analysis by the methodologies component includes the application of rules that act as multiple filters through which a transaction passes upon presentment of a check by a presenter, and wherein the filters include any one or more of a check amount threshold filter, a blocked maker filter, a data variance filter based on statistical analysis of historical performance, a new account filter based on several aspects of third-party validation of the presenter on first enrollment, a watch account filter, an amount variance filter, and a velocity filter.

16. The system of claim 11 further comprising:

a self-learning engine that facilitates an application, by the methodologies component, of past rules usage and transaction result records associated with analysis of checks during previously occurring transactions to the check cashing transaction.

17. The system of claim 11 wherein the rules-based analysis by the methodologies component includes analyzing pixels in the image of the check for deformation caused by copying or alteration.

18. The system of claim 11 wherein the rules-based analysis by the methodologies component includes analyzing the image obtained through scanning a paper stock on which the check is printed, wherein the analyzing includes determining whether alteration marks or voids exist in the paper stock for alteration.

19. The system of claim 11 wherein the rules-based analysis by the methodologies component includes integrating disparate third-party rules that represent fraud risk scenarios and translating the fraud risk scenarios into systematic rules for the purpose of assessing in real time transaction fraud risk.

20. The system of claim 11, further comprising:

a transaction history repository for storing new records associated with the check cashing transaction and past records associated with analysis of checks during previously occurring transactions, and wherein calculating the score includes scoring fields against the transaction history repository.

21. The system of claim 11, further comprising an administrative component for configuring and administering assisted workstations and automated transaction machines associated with the system, specifying the application of rules by the methodologies component, providing a user interface to a transaction repository associated with the system, maintaining user profiles associated with the system, and performing reports based on transactions, customers, and device status.

22. A system for facilitating the cashing of checks in a manner that reduces the cashing of potentially fraudulent checks, the system comprising:

means for capturing at least one image associated with a check to be cashed in a check cashing transaction, wherein the check to be cashed is issued by a check maker;

means for receiving transaction characteristics associated with the check cashing transaction;

means for extracting features and blocks of data from at least one document image associated with the check to be cashed;

means for verifying the existence of attributes from the at least one document image, including attributes associated with the extracted blocks of data;

means for generating and adapting a profile and a template for the check to be cashed, wherein the profile and the template facilitate further analysis of the check to determine whether the check should be cashed, and wherein the profile and the template are configured to facilitate cashing other checks issued from the check maker that issued the check; and

means for associating the generated template with a unique identifier, wherein the unique identifier is also associated with the check maker that issued the check.

23. The system of claim 22 wherein the means for capturing includes scanning components configured for scanning a front side and a back side of the check to be cashed.

24. The system of claim 22 wherein the transaction characteristics include information associated with at least one variable from a group of variables consisting of transaction amount, transaction date, transaction time, document date, customer information, location of presentment, device of presentment or check maker information, bank of maker information, transaction type, and document type.

25. The system of claim 22 wherein the extracted blocks data include one or more of a maker block, a logo block, an endorsement, a signature, watermark or security features, and a payee block.

26. The system of claim 22 wherein the attributes associated with the extracted blocks of data include one or more attributes obtained through feature extraction from a digitally obtained image, wherein the one or more attributes include:

signature, endorsement, notes, marks, annotations, courtesy amount recognition (CAR), and/or legal amount recognition (LAR).

27. A method for determining whether a transaction associated with a negotiable instrument should proceed, the method comprising:

applying rules to attributes of the negotiable instrument;

applying conditions against which no rule coverage is available; and

generating or suggesting new rules through self-learning, wherein the new rules are generated or suggested based on specific risk parameters.