Compromised Data Source Detection Using Fraud Proxy Scores
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Solution Overview
Problem
Existing systems for detecting data breaches in online transactions are reactive and rely on cardholder reports, which can take weeks or months to resolve, leaving compromised accounts vulnerable to further fraud.
Innovation Solution
A data analytics system that tracks transaction activity of payment devices after interacting with a merchant, generating fraud proxy scores and implication scores to identify potential data compromises, and automatically alerts receiving parties to take preventive measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If systems rely on cardholder reports for fraud detection, then fraud can be detected, but the detection process takes weeks or months to resolve, leaving accounts vulnerable to continued compromise
Solution Approach 1:
The system performs preliminary actions by proactively monitoring transaction patterns and generating implication scores before fraud is reported by cardholders. It tracks subsequent transactions at implicated merchants and identifies compromised accounts in advance, enabling preventive measures rather than reactive response.
Solution Approach 2:
The system implements feedback loops by continuously monitoring transaction activity, updating implication scores based on new transaction data, and adjusting fraud detection thresholds. This creates a dynamic system that learns from emerging patterns and adapts to new fraud schemes in real-time.
2Ease of operation
If payment card information is stored for recurring transactions and customer convenience, then checkout speed is improved, but the stored information becomes susceptible to data breaches and fraudulent use
Solution Approach 1:
The system acts as an intermediary layer between stored payment information and transaction processing. It monitors transactions involving stored card data, generates fraud risk scores, and can block suspicious transactions before they are completed, thereby protecting stored information without preventing legitimate use.
Solution Approach 2:
The system enables self-service fraud detection by automatically monitoring its own transaction data, generating implication scores, and identifying compromised accounts without requiring external intervention. This autonomous monitoring protects stored payment information while maintaining customer convenience.
3Measurement precision
If transaction monitoring is performed at individual merchants, then local fraud can be detected, but determining the source of breaches across multiple merchants becomes difficult
Solution Approach 1:
The system merges transaction monitoring across multiple merchants into a centralized platform. It aggregates transaction data from different merchants, combines implication scores, and provides a unified view of fraud patterns, making it easier to identify breach sources across the entire network rather than individually at each merchant.
Solution Approach 2:
The system provides universal fraud detection capabilities that work across multiple merchants and transaction types. The same implication score methodology and monitoring approach can be applied universally to detect fraud at any merchant, simplifying the process of identifying breach sources across diverse locations.
Data Source
AI summary
A compromise detection computing device is configured to receive transaction data associated with a set of transactions performed at a plurality of merchants, each transaction conducted using a payment device, identify a first merchant, and generate a list of every payment device that was used to conduct a transaction at the first merchant. The compromise detection computing device is further configured to monitor subsequent transaction activity associated with each payment device on the list, and generate a fraud proxy score for each payment device based upon the activity. The compromise detection computing device is further configured to access fraud report records associated with any payment device on the list, generate an implication score for the first merchant based upon the fraud proxy score and the fraud report records, and, when the first implication score meets a first criteria, automatically transmit an alert message to a receiving party.