Token Interaction Regression for Multi-Factor Fraud Risk Scoring
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Solution Overview
Problem
Current token transaction risk evaluation is based on binary decisions, failing to account for multiple authentication features, leading to inaccurate fraud assessment.
Innovation Solution
Implement a multiple variable regression formula to analyze a plurality of interaction features, determining a coefficient of determination, and adjusting slope coefficients to predict future transaction risks accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If binary decision method is used for token transaction risk evaluation, then the evaluation process is simple, but the accuracy of fraud assessment is insufficient
Solution Approach 1:
The patent transforms the binary risk evaluation (authorized/declined) into a multi-parameter regression analysis system. It introduces multiple authentication features as independent variables and uses regression coefficients to weight their contributions to risk assessment, enabling continuous risk scoring instead of binary decisions.
Solution Approach 2:
The patent segments the risk evaluation into multiple independent authentication features (token cryptogram validation, device binding verification, CDCVM validation, etc.), each contributing separately to the overall risk score through the regression formula, allowing granular analysis of different authentication aspects.
2Measurement precision
If multiple authentication features are considered, then the accuracy of risk assessment is improved, but the complexity of evaluation increases
Solution Approach 1:
The patent creates a universal regression-based evaluation framework that can handle multiple different authentication features through a single unified formula. The same regression model structure accommodates various authentication methods by simply plugging in different feature values, reducing the need for separate evaluation systems for each authentication type.
Solution Approach 2:
The patent replaces complex manual or rule-based multi-feature evaluation with an automated regression analysis system. The computer-implemented regression model automatically processes multiple authentication features and computes risk scores, substituting what would otherwise require complex manual assessment procedures.
3Measurement precision
If regression analysis with multiple variables is implemented, then the fraud detection accuracy is enhanced, but the computational complexity increases
Solution Approach 1:
The regression model is designed to be self-calibrating through iterative coefficient optimization. The system automatically adjusts the regression coefficients based on training data to maximize the coefficient of determination, reducing the need for manual tuning and simplifying deployment despite the underlying computational complexity.
Solution Approach 2:
The patent performs preliminary regression coefficient optimization during system setup or training phases. By pre-calculating and storing the optimal regression coefficients before actual risk evaluation, the system reduces real-time computational complexity while maintaining high fraud detection accuracy during transaction processing.
Data Source
AI summary
A method is disclosed. The method includes receiving interaction data related to a plurality of interactions in a time period, determining a multiple variable regression formula, and then determining a coefficient of determination associated with the multiple variable regression formula and the interaction data. The method further includes determining if the coefficient of determination satisfies a threshold or is maximized. If the coefficient of determination does not satisfy the threshold or is not maximized, then adjusting the slope coefficients. If the coefficient of determination does satisfy the threshold or is maximized, then using the multiple variable regression formula to determine risk associated with future interactions.