Real-Time Merchant Profile Fraud Detection
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Solution Overview
Problem
The rise of card not present (CNP) fraud, which accounts for over 50% of card fraud losses worldwide, poses a significant challenge as fraudsters exploit the lack of physical inspection in online transactions, making it difficult to detect testing behaviors indicative of fraudulent activities in real-time.
Innovation Solution
A system and method utilizing real-time merchant profiles (RTMP) and specialized scoring models to detect test events by monitoring deviations in transaction patterns, flagging probed cards, and adjusting fraud scores to prevent potential fraudulent use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time monitoring of transaction patterns is implemented to detect test events, then fraud detection capability is improved, but system complexity increases
Solution Approach 1:
The system segments fraud detection by creating separate real-time merchant profiles for different merchants, each tracking their own transaction patterns independently. This segmentation allows the system to monitor multiple merchants simultaneously without creating a monolithic complex system, as each merchant's data is processed and stored separately with its own baseline patterns.
Solution Approach 2:
The system performs preliminary actions by establishing baseline transaction patterns for each merchant before fraud detection is needed. Historical transaction data is collected and analyzed in advance to create expected behavior profiles, so that when real-time transactions occur, the system can quickly compare against pre-established baselines rather than analyzing patterns from scratch.
2Measurement precision
If specialized scoring models are applied to probed cards to enhance fraud detection, then fraud prevention accuracy is improved, but processing time increases
Solution Approach 1:
The system applies local quality by implementing specialized scoring models only for cards identified as probed or potentially fraudulent, rather than applying complex scoring to all transactions uniformly. Normal transactions use standard processing, while only suspicious transactions trigger the more time-consuming specialized analysis, thereby maintaining high accuracy for at-risk cases without significantly impacting overall processing time.
Data Source
AI summary
A system and method for detecting a test event involving a financial transaction device at a merchant having a merchant profile is disclosed. The method includes receiving data associated with a transaction involving a financial transaction device; calculating a score using at least the transaction data; comparing the score to a threshold value; and attaching a merchant probe flag to the merchant profile if the score exceeds the threshold value. The merchant probe flag indicates a likelihood that a test event has occurred at the merchant based on the score. If a test event has occurred, then financial transaction devices involved in the test event can have their profiles updated to reflect that they have been probed. If a financial transaction device that has been probed is used in a subsequent transaction, then a specialized fraud scoring model can be used to provide an improved fraud risk score.