Merchant Profile Builder for Fraud Detection Accuracy
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Solution Overview
Problem
Current fraud detection methods in credit card transactions primarily focus on cardholder profiles, neglecting the potential predictive value of merchant profiles, which could provide earlier detection of fraudulent behavior by identifying repeat interactions with fraudulent individuals.
Innovation Solution
A Merchant Profile Builder (MPB) system that processes authorization and fraud data to create robust merchant profiles, incorporating geographic information and historical transaction behavior, and integrates these profiles with existing fraud prediction systems for real-time scoring of transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fraud detection focuses only on cardholder profiles, then the detection system is simpler to implement, but the accuracy and timeliness of fraud detection deteriorates
Solution Approach 1:
The patent combines cardholder profiles with merchant profiles into a unified fraud detection system. The merchant profile builder integrates transaction data, authorization data, and fraud data from multiple sources to create comprehensive merchant profiles that are then merged with existing cardholder profile systems, enabling more accurate fraud detection through the combined information.
Solution Approach 2:
The patent segments the fraud detection system into distinct components: cardholder profile analysis, merchant profile building, and integrated fraud scoring. The merchant profile builder is further segmented into data collection, data processing, and profile generation stages, allowing each component to be optimized independently while maintaining overall system accuracy.
2Reliability
If merchant profile data is collected and processed, then fraud detection capability is improved, but data processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary action by pre-building and storing merchant profiles containing historical transaction patterns, authorization data, and fraud indicators before actual fraud detection is needed. This allows the system to quickly retrieve and compare merchant profile data during transaction processing, reducing real-time computational requirements and processing time.
Solution Approach 2:
The patent applies parameter changes by transforming raw transaction data into standardized profile parameters with specific weightings and thresholds. The system dynamically adjusts parameters such as fraud score thresholds, data retention periods, and processing intervals based on transaction types and risk levels, optimizing the balance between detection reliability and processing efficiency.
Data Source
AI summary
A merchant profile builder (MPB) is provided. The complete merchant profiling process is split into three pieces: Data Preprocessing, Weekly Summary Calculation, and Merchant Profile Calculation. In the Data Preprocessing stage, authorization data and daily fraud data are processed on a weekly basis to yield: an authorization extract file and fraud authorization data, each of which are sorted by a merchant key. In the Weekly Summary Calculation stage, the files output by the Data Preprocessing stage are used as input to compile basic statistics for each merchant based only on data for one week. There are separate weekly summaries for authorizations and fraud authorizations. In the Merchant Profile Calculation stage, the weekly summaries from the 16 most recent weeks are combined together and averages are calculated and output a set of merchant profiles and a set of default profiles.