Auto-tuning Rule Weights for Access Request Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing access request classification systems rely on manually assigned weights for rules, leading to inaccurate results due to guesswork, resulting in false negatives or false positives, and there is a need to optimize these weights automatically for improved accuracy and consistency.
Innovation Solution
A system that optimizes weights for access request classification rules using a cost function based on historical access requests, incorporating penalties for false positives and negatives, and computes a profile score to improve classification accuracy, using techniques like stepwise regression and machine learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual weight assignment is used for classification rules, then ease of operation is improved, but classification accuracy deteriorates
Solution Approach 1:
The system performs self-service by automatically optimizing rule weights through machine learning algorithms without requiring manual intervention. The optimization engine autonomously analyzes historical access requests and adjusts weights to maximize classification accuracy, eliminating the need for users to manually assign weights while achieving superior performance compared to manual methods.
Solution Approach 2:
The system dynamically changes the parameter of rule weights based on analysis of historical data and performance metrics. The optimization engine continuously adjusts weight values to optimize the cost function, transforming static manually-assigned weights into dynamic data-driven weights that adapt to changing fraud patterns and improve classification accuracy over time.
2Measurement precision
If automatic weight optimization is implemented, then classification accuracy is improved, but device complexity increases
Solution Approach 1:
The system segments the complex optimization task into distinct functional modules: a rule engine that evaluates access requests, an optimization engine that adjusts weights, and a cost function calculator that measures performance. This segmentation allows each component to specialize in specific operations, managing overall system complexity while achieving high classification accuracy through coordinated operation of specialized subsystems.
Solution Approach 2:
The optimization engine serves as an intermediary between the rule evaluation system and the weight configuration. It mediates the complex optimization process by automatically adjusting weights based on cost function analysis, shielding users from complexity while enabling automatic improvement of classification accuracy through data-driven weight optimization.
3Reliability
If more rules are added to the profile, then fraud detection capability is improved, but false positive rate increases
Solution Approach 1:
The system changes the parameters of rule weights dynamically based on their contribution to fraud detection versus false positives. The optimization engine analyzes the impact of each rule on classification performance and adjusts weights to maximize fraud detection while minimizing false positives, transforming a static rule set into a dynamically optimized ensemble that adapts to balance detection accuracy and false positive rates.
Solution Approach 2:
The system applies partial action by selectively emphasizing rules that provide high fraud detection value while de-emphasizing or suppressing rules that generate excessive false positives. The optimization engine identifies and weights only the most valuable rules appropriately, avoiding the need to either use all rules equally or manually curate the rule set, achieving optimal balance between detection capability and false positive control.
Data Source
AI summary
Disclosed is a system to optimize rule weights for classifying access requests so as to manage rates of false positives and false negative classifications. A rules suggestion engine may suggest a profile of classification rules to a merchant for access requests. The system can optimize weights for the profile of rules using a cost function based on a training set of historical access requests, for example using stepwise regression or machine learning (ML). The system can compute a profile score based on the optimized weights, for example by summing the weights. The system statistically analyzes the profile score using classification thresholds and the historical access requests. The system can perform receiver operating characteristic (ROC) analysis for various threshold values, enabling a user to select a suitable threshold. The system can further optimize by adding or removing rules from the profile of rules.