Analyzing Declined Payment Transactions for Fraud Accuracy
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Solution Overview
Problem
Payment networks and issuers face challenges in refining and evaluating the effectiveness of their fraud strategies due to limited reporting on declined transactions, which hampers their ability to accurately identify fraudulent activities and improve fraud detection models.
Innovation Solution
A system and method that utilize a research engine to analyze declined payment account transactions, append fraud accuracy tags to transactions based on transaction data, and provide insights into the accuracy of fraud models by distinguishing between true and false positives, enabling better input for fraud detection metrics and model refinement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fraud detection models use limited reporting data on declined transactions, then the complexity of data collection is reduced, but the measurement precision of fraud detection accuracy deteriorates
Solution Approach 1:
The system implements feedback by automatically reporting declined transaction data from issuers back to the payment network. This creates a closed-loop information flow where transaction decline data that would otherwise be lost is captured, analyzed, and fed back into the fraud detection system to improve future detection accuracy.
Solution Approach 2:
The system performs preliminary actions by proactively collecting and storing declined transaction data before it is lost. The automatic reporting mechanism captures this data at the source (issuers) and preserves it in a usable format for future analysis, preventing the information loss that would occur without such preliminary data capture.
2Reliability
If the system analyzes all declined transaction data from multiple issuers, then the reliability of fraud strategy evaluation is improved, but the device complexity increases
Solution Approach 1:
The payment network system performs multiple functions: it acts as a transaction routing network, a data collection platform, an analytics engine, and a fraud detection system. By making the payment network universal and multi-functional, the system can collect and analyze declined transaction data from multiple issuers without requiring separate dedicated infrastructure, thus improving reliability while managing complexity.
Solution Approach 2:
The system merges the data collection and analysis capabilities into the existing payment network infrastructure. Instead of creating separate complex systems for each issuer, the patent combines these functions into a unified platform that leverages the payment network's existing architecture, reducing overall system complexity while maintaining comprehensive data collection.
3Productivity
If fraud detection models are continuously refined with more transaction data, then the productivity of fraud prevention is improved, but the loss of time for data processing increases
Solution Approach 1:
The system enables continuous refinement of fraud detection models through automated, ongoing collection and analysis of declined transaction data. Rather than periodic batch processing, the system continuously captures data from issuers and updates fraud detection capabilities, maintaining continuous useful action that improves productivity while minimizing time loss through automation.
Solution Approach 2:
The system implements self-service by automatically collecting, processing, and analyzing declined transaction data without requiring manual intervention. The automated reporting from issuers and the self-acting analytics engine enable the system to refine fraud detection models continuously without human time investment, improving productivity while eliminating manual data processing time.
Data Source
AI summary
Disclosed are exemplary embodiments of systems and methods for analyzing declined payment account transactions. In an exemplary embodiment, a method generally includes accessing, by a computing device, a declined transaction associated with a payment account, and applying, by the computing device, at least one rule to transaction data associated with the payment account and/or transaction data associated with a merchant involved in the declined transaction. The method also includes, based on the at least one applied rule, appending, by the computing device, a fraud accuracy tag to the declined transaction in a data structure, where the fraud accuracy tag is indicative of whether the decline of the transaction is a true positive decline or a false positive decline, whereby the fraud accuracy tag is suitable to provide insight into accuracy of a fraud strategy implemented in connection with the declined transaction.