Fraud Risk Score Generation via Dynamic Model Combination
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Solution Overview
Problem
Current fraud risk prediction models fail to effectively address multiple types of identity fraud simultaneously due to their inability to account for distinct behavioral patterns, leading to limited performance and rapid degradation as fraudsters adapt their tactics, resulting in increased unmitigated fraud losses for financial services organizations.
Innovation Solution
A fraud risk determination system that employs multiple independent fraud component models, each predicting a specific type of fraud, and dynamically combines their individual predictors to generate a single identity fraud risk score, accounting for unique patterns and behaviors indicative of different types of fraud without averaging scores, thereby providing a comprehensive assessment of risk across various fraud types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single predictive model is used to predict multiple types of fraud, then the model complexity is reduced, but the measurement precision and reliability of fraud detection deteriorate due to inability to account for distinct behavioral patterns of different fraud types
Solution Approach 1:
The patent divides the fraud detection system into multiple independent fraud component models, each specializing in detecting a specific type of fraud (e.g., application fraud, account takeover, new account takeover). Each model independently analyzes transaction data for its designated fraud type, preserving detection precision while managing complexity through modular architecture. The individual fraud predictors from each component model are then combined to form a comprehensive fraud risk assessment.
2Measurement precision
If multiple independent fraud component models are used to predict different types of fraud, then the measurement precision and reliability of fraud detection improve, but the device complexity increases due to multiple models and dynamic weighting mechanisms
Solution Approach 1:
The patent merges the outputs of multiple independent fraud component models into a single unified fraud risk score. Each component model generates an individual fraud predictor, and these predictors are dynamically combined using weighted aggregation. The dynamic weighting mechanism adjusts the contribution of each component model based on current fraud patterns and model performance, achieving high detection precision while managing complexity through systematic integration.
Solution Approach 2:
The system employs dynamic weighting of individual fraud predictors, where the weight assigned to each fraud component model's output is not fixed but adjusts based on current conditions. This dynamic approach allows the system to adapt to evolving fraud patterns and varying relevance of different fraud types, maintaining high measurement precision while the computational complexity is managed through algorithmic weight adjustment rather than static complex architecture.
3Ease of operation
If a single fraud prediction model is used, then the ease of operation is improved, but the adaptability to different fraud tactics deteriorates as fraudsters adapt their methods
Solution Approach 1:
The patent creates a universal fraud detection system where multiple fraud component models serve different fraud types but are integrated into a single operational framework. Each component model is specialized for a particular fraud type, yet the overall system provides multi-functional capability to detect various fraud tactics. The dynamic weighting mechanism and unified risk score output maintain ease of operation while the modular component structure enables adaptability to different and evolving fraud methods.
Data Source
AI summary
A fraud risk determination system that provides a comprehensive approach to multiple different types of fraud that outputs a single identity fraud risk score based on dynamically combining several independent fraud component models which employ various analytical techniques.