CallCop

AI-Powered Real-Time Fraud Detection for Phone Calls (2024)

Project Overview

CallCop is an innovative AI-powered system designed to detect fraudulent phone calls in real-time. The project leverages a sophisticated combination of Twilio’s API, Large Language Models, and Firebase to provide seamless fraud detection during live phone conversations.

Problem Statement

With the increasing sophistication of phone scams and fraudulent calls, traditional methods of fraud detection are insufficient. Users need a real-time solution that can analyze conversations as they happen and alert them to potential fraud attempts during the call itself.

Solution Architecture

Our solution focuses on real-time fraud detection using a multi-layered approach:

  • Twilio Integration: Handles call routing and real-time audio processing
  • Google Speech-to-Text API: Converts live audio to text for analysis
  • Large Language Model: Analyzes transcriptions to detect fraudulent patterns
  • Firebase Backend: Manages data and provides scalable infrastructure
  • Flutter App: Provides a user-friendly interface for the system

How It Works

  1. Call Detection: When an unknown caller contacts a user, a Twilio agent automatically joins the call
  2. Audio Processing: The system captures real-time audio and converts it to text using Google’s speech-to-text API
  3. AI Analysis: Our machine learning model analyzes the transcription to identify fraudulent or suspicious behavior patterns
  4. Real-Time Alert: If fraud is detected, the Twilio agent immediately alerts the user during the call

Key Features

  • Real-time audio processing and transcription
  • Advanced LLM-based fraud detection
  • Seamless integration with existing phone systems
  • Immediate user alerts during suspicious calls

Project Scope

  • Seamless integration of Twilio for call handling
  • Google’s API for real-time speech transcription
  • Machine learning model for detecting fraudulent behavior
  • Real-time fraud detection through live transcription and analysis

Future Opportunities

The CallCop system has significant potential for expansion and enhancement:

Technical Enhancements

  • Multi-language Support: Expand to support more languages and dialects
  • Biometric Integration: Incorporate voice recognition for improved fraud detection accuracy
  • Advanced Analytics: Integrate with new channels APIs to stay updated with latest scam patterns

Platform Expansion

  • SMS Integration: Extend fraud detection to text messaging
  • Social Media: Expand to other digital communication platforms

GitHub Repository

The code for this project is available on GitHub.

Technologies Used

  • Backend: Twilio API, Firebase
  • AI/ML: Large Language Models, Custom ML algorithms
  • APIs: Google Speech-to-Text API, Twilio Voice API
  • Infrastructure: Firebase Cloud Functions, Real-time Database
  • Languages: Python, JavaScript, Node.js

CallCop represents a significant advancement in real-time fraud detection technology, combining cutting-edge AI with practical telephony solutions to protect users from increasingly sophisticated phone scams.