US 11,900,383 B2Granted

System, method, and computer program product for determining fraud rules

AssigneesVisa International Service Association, Board of Regents, The University of Texas System

InventorsYouxing Qu, Yiwei Cai, Dan Wang, Harishkumar Sundarji Majithiya, Roshni Ann Samuel, Susan Finnegan, Claudia Barcenas, Himanshu Chauhan

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Abstract

Methods for generating fraud detection rules based on transaction data may include receiving historical transaction data, associating tags with each transaction, generating decision trees having root nodes and child nodes operably connected to the respective root nodes, determining at least one primary rule and at least one set of secondary rules associated with the at least one primary rule based on relationships between features of the transactions, assigning primary rules and sets of secondary rules to the at least one decision tree to populate the tree, extracting a plurality of rule sets including at least one primary rule and one or more secondary rules, determining an ordering of the plurality of rule sets; and determining a subset of rule sets from the ordered plurality of rule sets against which subsequently received transactions are compared against to determine if the subsequent transactions are fraudulent.

Claims20

Dependent claims

A computer-implemented method comprising:

receiving, with at least one processor, transaction data associated with a plurality of transactions, the plurality of transactions including a plurality of non-fraudulent transactions and a plurality of fraudulent transactions;
generating, with at least one processor, at least one decision tree having a root node and at least one child node operably connected to the root node based on the transaction data associated with the plurality of transactions;
determining, with at least one processor, at least one primary rule and at least one set of secondary rules associated with the at least one primary rule based on the plurality of non-fraudulent transactions and the plurality of fraudulent transactions;
populating, with at least one processor, the at least one decision tree based on the at least one primary rule and the at least one set of secondary rules;
extracting, with at least one processor, a plurality of rule sets from the at least one decision tree;
determining, with at least one processor, an ordering of the plurality of rule sets based on memory-based efficiency characteristics and fraud-detection-based characteristics associated with each rule set of the plurality of rule sets;
and
determining, with at least one processor, whether the ordering of the plurality of rule sets satisfy a minimum efficiency threshold.

5 more elements

A system comprising:

at least one processor programmed or configured to:
receive transaction data associated with a plurality of transactions, the plurality of transactions including a plurality of non-fraudulent transactions and a plurality of fraudulent transactions;
generate at least one decision tree having a root node and at least one child node operably connected to the root node based on the transaction data associated with the plurality of transactions;
determine at least one primary rule and at least one set of secondary rules associated with the at least one primary rule based on the plurality of non-fraudulent transactions and the plurality of fraudulent transactions;
populate the at least one decision tree based on the at least one primary rule and the at least one set of secondary rules;
extract a plurality of rule sets from the at least one decision tree;
determine an ordering of the plurality of rule sets based on memory-based efficiency characteristics and fraud-detection-based characteristics associated with each rule set of the plurality of rule sets;
and
determine whether the ordering of the plurality of rule sets satisfy a minimum efficiency threshold.

6 more elements

A computer program product comprising at least one non-transitory computer readable medium including one or more instructions that, when executed by at least one processor, cause the at least one processor to:

receive transaction data associated with a plurality of transactions, the plurality of transactions including a plurality of non-fraudulent transactions and a plurality of fraudulent transactions;
generate at least one decision tree having a root node and at least one child node operably connected to the root node based on the transaction data associated with the plurality of transactions;
determine at least one primary rule and at least one set of secondary rules associated with the at least one primary rule based on the plurality of non-fraudulent transactions and the plurality of fraudulent transactions;
populate the at least one decision tree based on the at least one primary rule and the at least one set of secondary rules;
extract a plurality of rule sets from the at least one decision tree;
determine an ordering of the plurality of rule sets based on memory-based efficiency characteristics and fraud-detection-based characteristics associated with each rule set of the plurality of rule sets;
and
determine whether the ordering of the plurality of rule sets satisfy a minimum efficiency threshold.

5 more elements

Dependencies are inferred from the claim text.

Facts

Application: 17/686,607Filed 4 Mar 2022 · continuation
Priority: 8 Jan 2018
Published: US 2022/0391911 A18 Dec 2022
Granted: 13 Feb 2024
Term ends: Not estimatedThe source records don’t support an estimate.

Classification7 codes

G06Q 20/4016
Transaction verification: involving fraud or risk level assessment in transaction processing
G06N 5/01
Computing arrangements using knowledge-based models: dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
G06N 5/025
Knowledge engineering; Knowledge acquisition: extracting rules from data
G06N 20/20
Machine learning: ensemble learning

Family

Application62/614,668Us provisional application · filed 8 Jan 2018
PCT/US2019/012629Parent of this continuation · filed 8 Jan 2019
US 2022/0391911 A1Pre-grant publication · published 8 Dec 2022
Application16/960,593Parent of this continuation
US 11,288,674 B2Parent of this continuation
US 2020/0394659 A1Published application
US 11,900,383 B2This patent

From the US filing history. Foreign family members aren’t listed.