Macro Fraud Detection via Transaction Aggregation
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Solution Overview
Problem
Current fraud detection systems are ineffective in identifying and preventing macro level fraud, where multiple compromised transaction cards are used for small fraudulent transactions to evade detection thresholds.
Innovation Solution
A macro fraud detection system that aggregates historical transaction data into referential tables to determine threshold values, compares real-time transaction data to these thresholds, and implements alerting and limiting mechanisms when suspicious activity is detected, including evaluating velocity and unique primary account numbers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fraud detection thresholds are set to detect large fraudulent transactions, then large transaction fraud is prevented, but macro level fraud using multiple cards for small transactions evades detection
Solution Approach 1:
The system segments the fraud detection approach by analyzing transactions at multiple levels: individual card transactions and aggregate transactions across multiple cards. By dividing the detection scope into card-level and issuer-level aggregations, the system can detect both large single-card fraud and distributed multi-card macro fraud patterns simultaneously
Solution Approach 2:
The system merges transaction data from multiple cards and aggregates them by issuer and geographic region. This combining of data from numerous individual transactions enables the detection of macro level fraud patterns that would be invisible when examining single card transactions in isolation
2Measurement precision
If fraud detection systems monitor individual card transactions, then single card fraud is detected, but distributed fraud across multiple cards remains undetected
Solution Approach 1:
The system adds a new dimension to fraud detection by introducing geographic and organizational aggregation layers. Instead of only monitoring individual card transactions, the system aggregates data by issuer and region, creating a multi-dimensional detection framework that reveals macro fraud patterns without requiring complete redesign of existing single-card monitoring systems
3Reliability
If fraud thresholds are set low to detect small transactions, then small fraudulent transactions are detected, but false positives increase
Solution Approach 1:
By merging and aggregating transaction data across multiple cards and geographic regions, the system establishes baseline patterns of normal behavior. This aggregated view enables the system to distinguish between legitimate small transactions and fraudulent patterns more accurately, reducing false positives while maintaining sensitivity to actual fraud
Data Source
AI summary
A macro fraud detection system for detecting macro level fraud of transaction card automated teller machine (ATM) withdrawals includes a memory device for storing data and a processor. The processor is communicatively coupled to the memory device and is programmed to receive first transaction data corresponding to historical financial transactions between a plurality of transaction cards and at least one ATM. The processor is also programmed to aggregate the first transaction data into one or more referential tables, and determine one or more threshold values based on the aggregated first transaction data. In addition, the processor is programmed to receive second transaction data corresponding to current financial transactions between the plurality of transaction cards and the at least one ATM. Moreover, the processor is programmed to compare the second transaction data to the one or more threshold values.