Financial Fraud Detection Solution

Overview

This project provides an AI-powered solution to detect and prevent financial fraud, focusing on spam call detection, deepfake VKYC fraud prevention, and transaction monitoring. The solution leverages AWS cloud services to provide real-time, scalable, and privacy-compliant fraud detection mechanisms.

Features

1. Spam Call Detection:

   • Analyze call metadata and behavioral patterns to detect suspicious activities.
   • Utilize NLP techniques to analyze call transcripts for phishing and scam indicators.
   • Real-time call blocking and user alerts to mitigate risks.

2. Deepfake VKYC Detection:

   • Advanced computer vision and audio analysis to detect manipulated facial expressions and voice patterns.
   • Real-time differentiation between authentic and AI-generated inputs.

3. Transaction Monitoring:

   • Monitor transaction patterns to detect anomalies indicative of fraud or money laundering.
   • Ensure post-onboarding security through continuous analysis.

4. User Feedback & Reporting:

   • Enable users to report fraud and spam, improving the system via feedback loops.

AWS Services Used

• Amazon Connect: For call handling and analysis.
• Amazon Transcribe: Convert voice to text for further NLP analysis.
• Amazon Comprehend: Analyze text for scam indicators.
• Amazon Rekognition: Deepfake detection via facial analysis.
• AWS Lambda: Serverless execution for real-time processing.
• Amazon CloudWatch: Monitoring and alerting.
• Amazon DynamoDB: Store fraud detection records.

System Architecture

The solution follows a modular architecture:

1. Incoming call data -> Amazon Connect -> AWS Lambda (call analysis)
2. Call transcript -> Amazon Transcribe -> Amazon Comprehend
3. VKYC video -> Amazon Rekognition -> Fraud detection
4. Transaction data -> Machine Learning models for anomaly detection

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/financial-fraud-detection.git
   cd financial-fraud-detection

2. Install dependencies:

   pip install -r requirements.txt

3. Deploy AWS infrastructure using CloudFormation:

   aws cloudformation deploy --template-file deployment/cloudformation_template.yaml --stack-name fraud-detection-stack

Folder Structure

financial-fraud-detection/
├── README.md
├── LICENSE
├── .gitignore
├── architecture/
│   └── solution-architecture.png
├── src/
│   ├── spam_call_detection.py
│   ├── deepfake_detection_vkyc.py
│   ├── fraud_transaction_monitor.py
│   └── utils.py
├── models/
│   └── ml_model.pkl
├── deployment/
│   ├── cloudformation_template.yaml
│   └── lambda_functions/
│       ├── call_analysis_lambda.py
│       └── vkyc_verification_lambda.py
├── docs/
│   └── detailed_solution_documentation.md
└── requirements.txt

Usage

1. Run spam call detection:

   from src.spam_call_detection import detect_spam_call
   detect_spam_call("Your bank account is compromised, call now!")

2. Run deepfake detection:

   from src.deepfake_detection_vkyc import detect_deepfake
   detect_deepfake("sample_video.mp4")

3. Monitor transactions:

   from src.fraud_transaction_monitor import detect_fraudulent_transaction
   detect_fraudulent_transaction({"amount": 15000, "location": "Unknown"})

Contribution

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new feature branch.
3. Submit a pull request for review.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For any questions or support, please contact ruturajsonkamble29@gmail.com.