Visa International Service Association

Juharasha Shaik

Durga S Kala

Gajanan Chinchwadkar

Navendu Misra

Sam P Hamilton

Ram Miryala

Marc Corbalan Vila

Tathagata Sengupta

Stuart Mark Williams

Nipun Agarwal

Praveen Kumar Suresh Guggarigoudar

Ravi Rameshbhai Alagiya

Kavish Agarwal

Rajiv Ranjan

Debabrata Chowdhury

Kenny Tsai

Aditi Khare

Anuvind Pushpak

Xuepeng Li

Andrey Masharov

A method includes receiving historical interaction data, which includes a plurality of historical interactions. Each historical interaction is associated with a plurality of data fields. The method includes assigning a plurality of weights to the plurality of data fields, generating a neural network using the plurality of weights and the plurality of data fields, identifying a first plurality of feature indicators indicative of a first class, the first class being different from a second class; receiving a second plurality of feature indicators derived from data relating to compromised accounts, updating, a probability distribution component using the first plurality of feature indicators and the second plurality of feature indicators, and receiving current data for an interaction. The method also includes applying the probability distribution component to the current data, and scoring the interaction using the probability distribution component.

System for designing and validating fine grained fraud detection rules

Systems, methods, and computer program products may store, in a distributed cache, a rule associated with a plurality of accounts in a Real-Time Payments (RTP) network, the rule being stored in association with account data associated with the plurality of accounts; receive an account level exclusion directive associated with the account; store, in the distributed cache, the account level exclusion directive in association with the account; receive transaction data associated with a transaction in the RTP network between the account and another account; retrieve, from the distributed cache, the rule, the account level exclusion directive, and the account data associated with the account; exclude, based on the account level exclusion directive, use of the rule for processing the transaction; and process, without applying the rule, the transaction in the RTP network.

System, method, and computer program product for real-time account level rule exclusion for real-time payments

A computer-implemented method comprising providing a graphical user interface for user selection of fraud rules that comprises fraud transaction parameters determined based on transaction parameters included in a plurality of known fraudulent transactions. The GUI also comprises high risk merchants with a fraud rate that exceeds a threshold fraud rate. The method includes receiving a user selection of fraud rules relating to at least the fraud transaction parameters, the high risk merchants, and the high frequency merchants. The method includes receiving test transaction data including at least one transaction parameter and at least one merchant associated with the test transaction, applying the fraud rules to the received transaction data to identify probable fraudulent transactions.

Systems and methods for improved fraud detection

Some embodiments of the present invention relate to systems and methods for estimating validation time that use an estimation of the amount of data fulfilling a rule or a set of rules. This estimation method may be more accurate because it considers how much data will match the rule versus how much data can be discarded. The disclosed systems and methods give visibility into how much time a rule set validation will take and how long the computer system will be unavailable due to the rule set being evaluated. This allows users to make better decisions about whether and when a rule set should be evaluated, and/or whether and how to modify implementation of the rule set.

Systems and methods for estimating validation time for fraud detection rules

A method, system, and computer program product for fraud detection receive transaction data associated with a plurality of transactions; determine, based on the transaction data, that two or more consecutive transactions associated with a same account identifier include a value for a same at least one transaction parameter; in response to determining that each of the two or more consecutive transactions associated with the same account identifier include the value for the same at least one transaction parameter, determine a difference between the value for the at least one transaction parameter associated with a first transaction of the two or more consecutive transactions and the value for the at least one transaction parameter associated with a second transaction of the two or more consecutive transactions; and determine, based on the difference, that the two or more consecutive transactions are fraudulent transactions.

Method, system, and computer program product for fraud detection

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Foster City, CA, United States of America

Juharasha Shaik