Voice Print Scoring for Real-Time Fraud Detection
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Solution Overview
Problem
Current credit card fraud detection systems are ineffective in identifying fraud attempts, especially when a stolen identity is used to open new accounts or obtain new credit cards, as they rely on alerts from cardholders and mutual repository databases, which are often slow to detect fraudulent transactions, allowing perpetrators to commit multiple frauds before being suspected.
Innovation Solution
A method and apparatus that utilize voice analysis by scoring captured interactions against voice prints to generate fraud probabilities, employing a rule engine with dynamic thresholds and preprocessing techniques to identify fraudulent interactions in real-time, including emotion detection and speech-to-text analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional fraud detection methods using mutual repository databases and cardholder alerts are used, then fraud detection can be performed, but the detection time is delayed (one week to six months) allowing multiple fraud actions
Solution Approach 1:
The system performs preliminary voice print construction from available interactions before fraud detection is needed. Voice prints are built in advance from any captured interactions, allowing immediate comparison when fraud detection is required, eliminating the need to wait for traditional alert systems.
Solution Approach 2:
The patent replaces the mechanical system of manual fraud investigation and traditional database matching with automated voice biometric analysis. The scoring component automatically compares voice samples against voice prints using acoustic feature analysis, dramatically reducing detection time from weeks/months to real-time or near-real-time.
2Measurement precision
If voice scoring against multiple voice prints is performed to improve fraud detection accuracy, then more thorough analysis is achieved, but computational complexity increases
Solution Approach 1:
The system scores the voice sample against multiple voice prints (excessive action) to ensure thorough fraud detection. By comparing against several voice prints simultaneously, the system achieves high confidence in fraud probability determination, with the rule engine managing the complexity of coordinating multiple scoring operations.
Solution Approach 2:
The rule engine acts as an intermediary that manages the complexity of scoring against multiple voice prints. It coordinates the scoring operations, combines results, and applies thresholds to determine final fraud probability, simplifying the overall system architecture while maintaining high detection accuracy.
3Loss of time
If real-time fraud detection is implemented to reduce detection time, then immediate fraud prevention is possible, but system complexity and processing requirements increase
Solution Approach 1:
Voice prints are constructed in advance from captured interactions before they are needed for fraud detection. This preliminary preparation of biometric templates enables real-time comparison without requiring complex real-time voice processing, reducing both detection time and real-time computational requirements.
Solution Approach 2:
The system creates voice print copies (biometric templates) from original voice samples. These copied representations contain the essential acoustic features needed for comparison but require less computational resources to process in real-time than analyzing full original audio recordings.
Data Source
AI summary
A fraud detection method for generating a first fraud or fraud attempt probability, within an at least one captured or recorded interaction, is provided. The method comprises a scoring step for scoring an at least one voice belonging to an at least one tested speaker in the at least one captured or recorded interaction against an at least one voice print within an at least one entry in a voice print collection, the scoring step generating an at least one probability that the at least one voice in the captured or recorded interaction belongs to an at least one second speaker associated with the at least one voice print, said at least one probability represents the probability that the at least one captured or recorded interaction is fraudulent; and an auditing step for auditing the at least one probability and the at least one captured or recorded interaction.