AI Fraud Detection System with Proactive Caller Interaction
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Solution Overview
Problem
Current systems for detecting phone frauds and scams are inadequate in effectively identifying and mitigating telemarketing scams, particularly affecting vulnerable groups like the poor, elderly, and immigrants without strong English skills, as they often rely on monitoring inbound calls and do not proactively interact with callers to prevent fraudulent activities.
Innovation Solution
A system and method that utilizes AI and machine learning to interact with callers before they reach the recipient's device, determining fraud likelihood by analyzing caller characteristics and playing commercial advertisements to waste scammers' time, thereby reducing unwanted sales calls and scams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current fraud detecting systems monitor inbound calls by features such as suspect numbers and risk factors, then fraud detection capability is provided, but the systems cannot proactively interact with callers to prevent fraudulent activities
Solution Approach 1:
The system performs preliminary actions by interacting with callers before the call reaches the recipient. The AI assistant engages callers in conversation, asks questions about their purpose, and attempts to identify fraudulent intent before the victim is exposed to the scam, thereby preventing fraud rather than just detecting it after the fact.
Solution Approach 2:
The patent introduces an AI assistant as an intermediary between the caller and the recipient. This intermediary entity conducts preliminary interactions, evaluates caller credibility, and can block or alert about potentially fraudulent calls, resolving the contradiction by adding a proactive detection layer without requiring direct user involvement.
2Measurement precision
If AI and machine learning are used to interact with callers and analyze characteristics, then fraud identification accuracy is improved, but system complexity increases
Solution Approach 1:
The AI assistant operates autonomously to evaluate caller credibility by analyzing voice characteristics, language patterns, and response consistency. The system self-services the fraud detection function without requiring human operators to manually analyze each call, thereby improving accuracy while managing complexity through automation.
Solution Approach 2:
The system monitors multiple dynamic parameters including voice tone, speech rate, pause duration, and linguistic complexity to detect fraudulent patterns. By changing and monitoring these parameters in real-time during the interaction, the system achieves high identification accuracy without requiring overly complex infrastructure.
3Reliability
If commercial advertisements are played to waste scammers' time, then fraud prevention is enhanced, but call duration increases
Solution Approach 1:
The system applies preliminary anti-action by playing commercial advertisements or holding messages during the AI-assisted preliminary interaction with the caller. This delays or prevents the fraudulent call from reaching the recipient, wasting the scammer's time and resources while protecting the victim, thereby enhancing fraud prevention despite the extended call duration.
Data Source
AI summary
A phone frauds or scams detecting system comprises a first detecting unit for obtaining callers' characteristics and a second detecting unit for interacting with callers and obtaining callers' additional characteristics. The system determines frauds or scams based on the callers' characteristics and the callers' additional characteristics and plays commercial advertisements to the callers if one or many frauds or scams determined.