Fraud Prevention Rule Optimization via Hyper-Rectangle Data Segmentation
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Solution Overview
Problem
Financial institutions face challenges in effectively managing fraud prevention due to the complexity and cost of maintaining technical infrastructure, as well as a lack of expertise in writing and optimizing fraud reduction strategies, leading to significant losses from fraudulent transactions.
Innovation Solution
A method that uses historical transaction data to optimize fraud prevention rules by defining a hyper-rectangle in a multi-dimensional space, removing and adding points to maximize or maintain a target variable's mean value, and identifying authorization business rules that define a minimum bounding box to improve fraud detection and authorization strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional fraud prevention rules are used, then fraud detection capability is maintained, but false positive rate increases and low-risk transactions are incorrectly declined
Solution Approach 1:
The patent transforms fixed threshold parameters into dynamic, data-driven parameters. The optimization engine analyzes historical transaction data to determine optimal threshold values that adapt to changing fraud patterns, allowing the system to maintain high fraud detection accuracy while reducing false positives and improving transaction approval rates for low-risk transactions.
Solution Approach 2:
The system implements self-optimizing fraud prevention rules that automatically learn from historical data without requiring manual intervention. The optimization engine continuously processes transaction data to refine rules and thresholds, enabling the system to autonomously improve its detection accuracy while minimizing impact on legitimate transactions.
2Reliability
If manual fraud strategy development is used, then expert intuition can be applied, but the process is time-consuming and lacks scalability
Solution Approach 1:
The patent replaces manual, mechanical rule development processes with an automated optimization engine that uses data-driven algorithms. This substitution eliminates the time-consuming manual analysis and rule creation process while maintaining or improving strategy effectiveness through systematic optimization of fraud detection parameters based on historical transaction data.
Solution Approach 2:
The optimization engine serves as an intermediary between raw transaction data and fraud prevention rules. It processes historical data, identifies patterns, and generates optimized rules that bridge the gap between data and actionable fraud prevention strategies, automating what previously required manual expert analysis.
3Reliability
If comprehensive fraud monitoring is implemented, then fraud detection capability is improved, but system complexity and maintenance costs increase
Solution Approach 1:
The patent extracts the complex optimization logic from the core transaction processing system and places it in a separate optimization engine. This extraction allows comprehensive fraud monitoring to be implemented without increasing the complexity of the main payment processing infrastructure, as the optimization engine operates independently to analyze historical data and generate rules.
Solution Approach 2:
The system segments fraud prevention into distinct functional components: the optimization engine that analyzes historical data and generates rules, and the rule execution engine that applies rules to transactions. This segmentation allows comprehensive monitoring capabilities to be implemented in modular fashion, reducing overall system complexity and making maintenance more manageable.
Data Source
AI summary
Rules, applied to deny authorization of likely fraudulent transactions, are derived from a modified Patient Rule Induction Method algorithm that uses a target variable and a data set of past transactions each associated with a plurality of input variables and a hyper-rectangle enclosing a multi-dimensional space defined by a representation of the input variable values as points within the multi-dimensional space. While a count of the points within the hyper-rectangle is greater than a minimum support parameter, a first plurality of points proximal to edges of the hyper-rectangle are removed, where each such removing maximizes a mean value of the target variable, and then, while the mean value remains maximized, a second plurality of points proximal to the edges is added, where each adding maximizes or maintains the mean value. The hyper-rectangle is bounded within a minimum bounding box that defines the rules.