Biometric Measures Profiling Analytics for Fraud Detection
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Solution Overview
Problem
Existing biometric systems face challenges in accurately and efficiently collecting and utilizing biometric data due to high friction levels, leading to increased false positives and reduced adoption, particularly in the general population, where privacy concerns and device consistency issues hinder effective fraud detection and authentication.
Innovation Solution
The use of low-friction or no-friction biometric data gathering methods, combined with Recursive Frequency Lists and Global Quantile estimation, to construct biometric measurement profiles that track normal and abnormal data patterns, allowing for unsupervised or supervised analytics to generate behavioral scores and blend with existing fraud scores, thereby reducing false positives and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-friction biometric collection methods (facial recognition, retinal scan, handwriting analysis) are used to improve fraud detection accuracy, then measurement precision improves, but ease of operation deteriorates and customer friction increases
Solution Approach 1:
The patent extracts and analyzes subtle biometric signals from existing transaction data and device usage patterns rather than requiring dedicated biometric collection processes. By taking out meaningful patterns from routine customer interactions (purchase behavior, device usage, timing patterns), the system achieves biometric-level accuracy without the friction of formal biometric collection procedures.
Solution Approach 2:
The patent introduces an intermediary analytical layer that processes existing transaction and device data to generate biometric-like profiles. This intermediary processing transforms routine transaction data into meaningful behavioral patterns that serve as proxies for traditional biometrics, enabling fraud detection without direct customer interaction with biometric collection interfaces.
2Measurement precision
If traditional biometric collection processes are implemented to enhance fraud detection, then measurement precision improves, but device complexity increases due to multiple capture devices and administration requirements
Solution Approach 1:
The patent makes existing transaction processing systems multi-functional by enabling them to simultaneously handle routine transactions and extract biometric-like behavioral patterns. The same infrastructure that processes payment transactions also analyzes customer behavior patterns, device usage sequences, and purchasing habits to generate fraud risk profiles, eliminating the need for separate biometric collection systems.
Solution Approach 2:
The system performs self-service analysis by automatically extracting and analyzing behavioral patterns from existing transaction data without requiring external biometric collection infrastructure. The system serves its own fraud detection needs by processing its existing data streams through pattern recognition algorithms, eliminating dependency on external biometric devices and administration processes.
3Adaptability or versatility
If biometric data is collected from diverse user populations with varying opt-in rates and capture qualities, then adaptability improves, but measurement precision deteriorates due to inhomogeneous data quality and false positives
Solution Approach 1:
The patent applies local quality analysis by examining and weighting different aspects of customer behavior patterns according to their specific relevance and reliability for each individual customer. The system identifies which behavioral patterns are most indicative of fraud for each customer based on their unique purchasing history, device usage patterns, and contextual factors, creating customized fraud detection thresholds that account for individual variations in behavior.
Solution Approach 2:
The patent dynamically adjusts detection parameters and thresholds based on individual customer profiles and contextual factors. Rather than using fixed biometric thresholds, the system changes its analysis parameters according to each customer's historical behavior patterns, device ecosystem, and risk context, enabling accurate fraud detection across diverse populations while adapting to individual variations in behavior and device capabilities.
Data Source
AI summary
A biometric measures profiling analytics system and method are presented. The system and method include collecting biometric data associated with a consumer, and determining one or more biometric variables representing a measurable aspect of the biometric data. The system and method further include generating, based on at least one of the one or more biometric variables, at least one biometric profile variable associated with the consumer, the at least one biometric profile variable representing a degree of normality or abnormality of the collected and calibrated biometric data as compared to a biometric history of the consumer. The system and method further include generating a behavioral score for the consumer based on the collected and calibrated biometric data and with at least one biometric profile variable, the behavioral score representing a degree of risk of normality or abnormality of an event associated with the biometric data.