🛡️ Adaptive AI Fraud Prevention System

Monitor, Detect, and Prevent Insurance Fraud Using AI in Real-Time

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🔍 Overview

The Adaptive AI Fraud Prevention System is a comprehensive and scalable solution that leverages advanced AI technologies to identify and mitigate fraudulent activities throughout the insurance lifecycle. It integrates machine learning, document forensics, graph-based behavioral analysis, and federated learning to deliver real-time fraud intelligence.

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🚀 Demo

Here's a quick preview of the system UI (mockup):

Interactive dashboard showcasing real-time alerts, document verification, and fraud trends.

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⚙️ Features

• ✅ Dynamic Fraud Detection
  Employs ML models like XGBoost, LightGBM, and Autoencoders to detect anomalies and score fraud risk.

• 🧾 Document Forgery Detection
  Detects tampered or altered documents using OCR pipelines and deep learning models.

• 🧠 Behavioral Profiling (In Progress)
  Utilizes Graph Neural Networks (GNNs) to analyze relationships between entities and identify collusion rings.

• 🔐 Federated Learning (In Progress)
  Enables insurers to collaboratively train models without exposing sensitive data.

• 🧪 Fraud Simulation (In Progress)
  Uses Generative Adversarial Networks (GANs) to synthesize fraud scenarios for robust training.

• ⚡ Real-Time Detection (In Progress)
  Integrates lightweight models at the ingestion stage for instant flagging.

• 📊 Interactive Dashboard
  Built with Streamlit, offering real-time visualization of fraud trends and alert systems.

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🧰 Tech Stack

• Frontend/UI: Streamlit
• Machine Learning: Scikit-learn, XGBoost, LightGBM, Autoencoders
• Deep Learning: PyTorch / TensorFlow
• Graph AI: PyTorch Geometric / DGL
• OCR: Tesseract / EasyOCR
• Data Management: Pandas, NumPy
• Model Sharing: Federated Learning via Flower or PySyft

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🛠️ Installation & Setup

1. Clone the Repository

git clone https://github.com/yourusername/adaptive-ai-fraud-prevention.git
cd adaptive-ai-fraud-prevention

2. Create a Conda Environment

conda env create -f environment.yml
conda activate fraud-detection

✅ If you're not using Conda, create a virtual environment manually and install dependencies via pip.

3. Install Additional Python Dependencies

pip install -r requirements.txt

4. Run the Project

streamlit run app.py

📝 You can also test specific modules using:

python test.py

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📁 Project Structure

adaptive-ai-fraud-prevention/
│
├── app.py                    # Main Streamlit app
├── test.py                   # Test module for models
├── models/                   # Trained ML/DL models
├── utils/                    # Preprocessing and helper functions
├── data/                     # Sample datasets
├── assets/                   # Images & mockups
├── environment.yml           # Conda environment file
└── requirements.txt          # Pip requirements

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🧪 Coming Soon

• 🔁 API Integration for Insurance Databases
• 📱 Mobile-responsive Dashboard
• 🌐 Cloud Deployment with Docker + GCP

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🤝 Contributors

• @Mrigank118
• @Jainritikaa

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