Automated Rules Management for Fraud Detection
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Solution Overview
Problem
Conventional electronic security measures for detecting fraudulent transactions, such as those using machine learning models and rules defined by human experts, face degradation over time and are computationally expensive, leading to inefficiencies in detecting security breaches and requiring costly maintenance.
Innovation Solution
An automated rules management system that optimizes a set of evaluation rules by modifying rule activations and priorities to improve the performance of fraud detection systems, using techniques like genetic programming and user-defined metrics to enhance precision and recall, thereby reducing false positives and negatives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional electronic security measures use machine learning models and rules defined by human experts to detect fraudulent transactions, then detection capability is improved, but computational cost and maintenance expense increase
Solution Approach 1:
The patent implements dynamic rule optimization where the rule set is continuously adapted based on performance metrics. The system automatically modifies rule priorities and activations over time to maintain detection effectiveness while reducing computational overhead, transitioning from static expert-defined rules to dynamic optimized rules
Solution Approach 2:
The system changes parameters of the rule set including rule priorities, activation states, and threshold values through automated optimization processes. This allows the same rule structure to achieve improved performance with reduced computational cost by adjusting parameter configurations rather than maintaining complex rule logic
2Measurement precision
If more rules are activated to improve fraud detection precision and recall, then detection accuracy is improved, but the number of false positives and computational expenses increase
Solution Approach 1:
The system applies partial activation of rules based on optimization results, activating only the subset of rules that contribute positively to detection accuracy while minimizing false positives. This avoids the excessive action of activating all possible rules, achieving better precision-recall balance with fewer false positives
Solution Approach 2:
The system uses feedback from performance metrics including false positive rates and detection accuracy to continuously optimize the rule set. By monitoring outcomes and adjusting rule configurations based on this feedback, the system maintains high detection accuracy while minimizing harmful false positives
3Reliability
If rule performance is maintained over time through continuous monitoring and optimization, then detection effectiveness is preserved, but system complexity and optimization computational cost increase
Solution Approach 1:
The system implements self-service optimization where the rule management system automatically monitors its own performance and optimizes its rule set without external intervention. This self-managing capability preserves detection effectiveness while avoiding the need for complex external optimization infrastructure
Solution Approach 2:
The system performs preliminary optimization actions by pre-processing and evaluating rule configurations before deployment. This proactive approach maintains performance reliability by addressing potential degradation before it occurs, rather than requiring complex reactive optimization systems
Data Source
AI summary
In an embodiment, a process for automated rules management system includes receiving a specification of past predicted results of evaluation rules and corresponding observed outcomes. The process includes determining one or more sets of alternative activations or priorities of at least a portion of the evaluation rules, assessing the one or more sets of alternative activations or priorities of at least a portion of the evaluation rules, and optimizing result activations or priorities of at least a portion of the evaluation rules based at least in part on the assessment of the one or more sets of alternative activations or priorities.