Voiceprint Fraud Detection via Dynamic Database Updates
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for credit card transactions online are inadequate in preventing identity theft, leading to public perception of increased risk and slowed commerce, as they fail to effectively deter repeat offenders.
Innovation Solution
A system that uses voice recognition to compare voice samples against a database of known fraudulent voice signatures, denying transactions if a match is found and adding new voice signals to the database if verification fails, thereby creating a dynamic security layer for credit card transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional credit card verification methods are used, then transaction simplicity is maintained, but fraud detection capability deteriorates
Solution Approach 1:
The verification process is segmented into distinct phases: initial voiceprint enrollment, transactional voice verification, and database lookup. This segmentation allows the system to maintain simplicity in each individual step while achieving comprehensive fraud detection through the combination of these steps.
Solution Approach 2:
The system performs preliminary voiceprint enrollment during account setup, creating a reference template before actual transactions occur. This preliminary action enables rapid verification during transactions without adding complexity to the payment process itself.
2Reliability
If voice recognition technology is implemented, then repeat offender detection is improved, but system complexity increases
Solution Approach 1:
The system creates voiceprint copies - digital representations of vocal characteristics - and stores them in a database. These copies can be rapidly compared against transactional voice samples without requiring complex real-time analysis, simplifying the verification process while maintaining high detection accuracy.
Solution Approach 2:
The patent replaces traditional mechanical verification methods (signature pads, PIN entry) with acoustic field-based voice recognition. This substitution uses biological vocal characteristics that are difficult to replicate, providing superior repeat offender detection while the automated processing keeps system complexity manageable.
3Reliability
If a database of fraudulent voice signatures is maintained, then fraud prevention is enhanced, but data storage requirements increase
Solution Approach 1:
The voiceprint database is organized with local quality optimization - storing only essential vocal characteristic parameters rather than complete audio recordings. This selective storage of critical features (pitch, timbre, speech patterns) reduces storage requirements while maintaining effective fraud prevention capability.
Data Source
AI summary
Disclosed are systems, methods, and computer readable media for comparing customer voice prints comprising of uncommonly spoken words with a database of known fraudulent voice signatures and continually updating the database to decrease the risk of identity theft. The method embodiment comprises comparing a received voice signal against a database of known fraudulent voice signatures, denying the caller's transaction if the voice signal substantially matches the database of known fraudulent voice signatures, adding the caller's voice signal to the database of known fraudulent voice signatures if the voice signal does not substantially match a separate speaker verification database and received additional information is not verified.