Generative AI Video Verification for Fraud Prevention
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Solution Overview
Problem
Conventional verification techniques, particularly knowledge-based verification, have become unreliable due to the widespread adoption of social networks and the ability of AI to scrape private data, leading to increased risks of account hacking and fraudulent takeovers.
Innovation Solution
A video verification framework that utilizes generative artificial intelligence to dynamically generate questions based on real-time user data, incorporating machine learning for enhanced security and risk management, thereby providing a secure and efficient verification process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If knowledge based verification is used, then verification process is simple and efficient, but security and reliability deteriorate due to AI scraping social networks
Solution Approach 1:
The verification system dynamically generates questions in real-time based on user behavior analysis and risk assessment, rather than using static pre-defined questions. This dynamic adaptation prevents AI systems from scraping and answering fixed questions, thereby maintaining security while preserving verification efficiency
Solution Approach 2:
The system changes the parameters of verification by transitioning from fixed knowledge-based questions to dynamically generated questions that vary based on multiple factors including user behavior patterns, risk levels, and real-time data. This parameter change ensures that verification remains efficient while adapting to new security threats
2Reliability
If dynamic question generation is implemented, then verification security is improved, but system complexity increases
Solution Approach 1:
The system employs a multi-functional framework that integrates various components including behavior analysis engines, risk assessment models, and dynamic question generation algorithms into a unified verification system. This universal approach handles multiple verification scenarios and threat types through a single complex system, improving security without requiring multiple separate systems
Solution Approach 2:
The patent introduces intermediary components such as behavior analysis engines and risk assessment models that mediate between user inputs and verification decisions. These intermediaries process and analyze data to generate appropriate verification questions, managing system complexity by breaking down the verification process into manageable functional layers
3Ease of operation
If conventional verification methods are used, then ease of operation is maintained, but vulnerability to fraud increases
Solution Approach 1:
The verification system performs self-service by automatically analyzing user behavior patterns and generating appropriate verification questions without requiring manual intervention from agents. This automated self-service maintains ease of operation for users while effectively detecting and preventing fraudulent activities through continuous behavioral analysis
Data Source
AI summary
Systems, methods, and computer program products for a video verification system with generative artificial intelligence are provided. A generative artificial intelligence model generates a dynamic set of questions based on account, identity, risk, and compliance associated with a user. The questions in the set correspond to different difficulty levels for answering the question. A question from the set is communicated to a computing device of a user over a video call with an artificial intelligence bot. In response, the video verification system receives an answer to the question, and one or more of a telemetry, an audio, and a video. The answer, telemetry, audio, and video data are assessed to determine a difficultly level of a subsequent question. The subsequent question is selected from the dynamic set of questions using the difficulty level and provided to the computing device of a user over the video call.