Risk assessment rule set application for fraud prevention

Patent metadata

Field	Value
Publication number	US-9842336-B2
Application number	US-201414585067-A
Country	US
Kind code	B2
Filing date	Dec 29, 2014
Priority date	May 7, 2009
Publication date	Dec 12, 2017
Grant date	Dec 12, 2017

Abstract

Rules, applied to deny authorization of likely fraudulent transactions, are derived from a modified Patient Rule Induction Method algorithm that uses a target variable and a data set of past transactions each associated with a plurality of input variables and a hyper-rectangle enclosing a multi-dimensional space defined by a representation of the input variable values as points within the multi-dimensional space. While a count of the points within the hyper-rectangle is greater than a minimum support parameter, a first plurality of points proximal to edges of the hyper-rectangle are removed, where each such removing maximizes a mean value of the target variable, and then, while the mean value remains maximized, a second plurality of points proximal to the edges is added, where each adding maximizes or maintains the mean value. The hyper-rectangle is bounded within a minimum bounding box that defines the rules.

First claim

What is claimed is:

1. A computer-implemented method for determining and for denying the authorization of fraudulent financial transactions, the method comprising:
receiving, via a communication network, by one or more processors, a request to authorize a transaction between a merchant and an account holder on an account issued by an issuer, wherein the transaction includes associated transaction data;
generating, via the one or more processors, a data structure defining a hyper-rectangle enclosing a multi-dimensional space defined by a plurality of edges, said data structure including data fields storing data for each of the plurality of edges of the hyper-rectangle, said plurality of edges representing a plurality of variable values;
retrieving, via the one or more processors, a target optimization variable associated with a data set;
in response to the determination that a number of points within the hyper-rectangle is greater than a minimum support parameter, concurrently removing, via the one or more processors, a first plurality of the points proximal to the plurality of edges;
in response to the determination that the mean value of the target optimization variable is maximized, adding, via the one or more processors, a second set of data fields representing a second plurality of points proximal to the plurality of edges;
bounding, via the one or more processors, the hyper-rectangle within a minimum bounding box, wherein one or more authorization business rules define the minimum bounding box, wherein the minimum bounding box defines a subspace defined by only those dimensions involved in the removing and the adding;
determining, via the one or more processors, a plurality of fraud prevention boundary limits based on the determined minimum bounding box;
determining, via the one or more processors, whether the transaction is fraudulent based on the application of the plurality of fraud prevention boundary limits on the received transaction data; and
in response to the determination that the transaction is fraudulent, transmitting, via the one or more processors, via the communication network, a denial response to the request to authorize the transaction.

2. The method as defined in claim 1 , further comprising in response to the determination that the transaction is fraudulent, notifying the issuer of the account.

3. The method as defined in claim 1 , further comprising:
determining whether the transaction is eligible for backup processing; and
in response to the determination that the transaction is eligible for backup processing, applying a backup processing procedure to process the transaction.

4. The method as defined in claim 3 , further comprising:
in response to the determination that the transaction is not eligible for backup processing, referring for processing the transaction to the issuer of the account upon which the transaction was being conducted.

5. The method as defined in claim 1 , further comprising:
identifying, from the transaction data, at least one of i) an account number and ii) a Bank Identification Number (BIN);
determining whether the at least one of the account number and the BIN is a real-time decisioning participant; and
in response to the determination that the at least one of the account number and the BIN is a real-time descisioning participant, determining whether the transaction is fraudulent in real-time.

6. The computer implemented method of claim 1 , wherein the plurality of input variable values respectively correspond to a plurality of transaction parameter input variables that are represented as points within the multi-dimensional space.

7. The computer implemented method of claim 6 , wherein the hyper-rectangle includes a plurality of edges, each edge associated with one of the plurality of transaction parameter input variables.

8. The computer implemented method of claim 7 , wherein the plurality of transaction parameter input variables is associated with the data set.

9. The computer implemented method of claim 8 , wherein the data set corresponds to information from a plurality of historical transactions and each historical transaction was conducted between one of a plurality of merchants and one of a plurality of account holders on a corresponding account of the one of plurality of account holders issued by one of a plurality of issuers.

10. The method of claim 1 , wherein maximizing a mean value of the target optimization variable for the hyper rectangle further comprises removing, via the one or more processors, a first plurality of points proximal to the plurality of edges, wherein said removing maximizes a mean value of the target optimization variable for the hyper-rectangle.

11. The method of claim 1 , wherein bounding, via the one or more processors, the hyper-rectangle within a minimum bounding box further comprises defining dimensions for the minimum bounding box.

12. The method of claim 1 , wherein defining dimensions for the minimum bounding box comprises at least the removal of the first plurality of points and the addition of the second plurality of points.

13. The method as defined in claim 1 , further comprising obtaining the minimum support parameter.

14. The method as defined in claim 1 , further comprising obtaining a maximum peeling parameter, wherein the maximum peeling parameter is the maximum count of the first plurality of points that is removable during said removing.

15. The method as defined in claim 1 , further comprising obtaining a condition associated with at least one of the plurality of transaction parameter input variables, wherein the multi-dimensional space is defined by the plurality of input variable values that satisfy the condition.

16. The method as defined in claim 1 , wherein the plurality of fraud prevention boundary limits correspond to the edges of the minimum bounding box.

17. The method as defined in claim 1 , wherein the transaction parameter input variable to be optimized is one of: a mean sale amount for a merchant, a number of chargebacks; a transaction volume; and a number of fraudulent transactions.

18. A computer-readable medium having instructions stored thereon and executable by one or more processors to perform a method for determining and for denying the authorization of fraudulent financial transactions, the method comprising:
receiving, via a communication network, by the one or more processors, a request to authorize a transaction between a merchant and an account holder on an account issued by an issuer, wherein the transaction includes associated transaction data;
generating, via one or more processors, a data structure defining a hyper-rectangle enclosing a multi-dimensional space defined by a plurality of edges, said data structure including data fields storing data for each of the plurality of edges of the hyper-rectangle, said plurality of edges representing a plurality of variable values;
retrieving, via the one or more processors, a target optimization variable associated with a data set;
in response to the determination that a number of points within the hyper-rectangle is greater than a minimum support parameter, concurrently removing, via the one or more processors, a first plurality of the points proximal to the plurality of edges;
in response to the determination that the mean value of the target optimization variable is maximized, adding, via the one or more processors, a second set of data fields representing a second plurality of points proximal to the plurality of edges;
bounding, via the one or more processors, the

Assignees

• Visa Usa Inc
• Visa Int Service Ass

Inventors

• Liu Frederick
• Hilgers Nancy
• Nelsen Mark
• Siegel Kevin
• Brown Christopher
• Dwyer Thomas

Classifications

• G06Q20/405

  Establishing or using transaction specific rules · CPC title

• G06Q20/40

  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 title

• G06Q20/4016 Primary

  involving fraud or risk level assessment in transaction processing · CPC title