Automated Fraud Detection System for Country’s Biggest Insurance provider

The Automated Fraud Detection System is a cutting-edge solution designed to detect and prevent fraudulent insurance claims efficiently. Built using Python, Flask, and advanced machine learning algorithms, the system automates the processing of PDF documents, extracts key features, and analyzes new claims against historical data to identify potential fraud.

This intelligent system integrates with external APIs, leverages image hashing techniques, and employs deep learning models for accurate feature extraction and classification. By combining data-driven insights with real-time fraud detection, the solution enhances security and minimizes financial losses for insurance providers.

About Client:

Generation Stack developed this solution for the largest insurance provider in the country, which operates multiple subsidiary businesses. The client required an advanced fraud detection mechanism to safeguard against fraudulent claims while streamlining claims processing.

Client Requirements:

The client required a robust and intelligent system to enhance fraud detection and streamline claims processing. The key requirements included:

• Detect Duplicate Claims : Identify and flag claims that have already been processed to prevent duplicate submissions.
• Classify Documents : Automatically categorize images within PDFs, such as bills, IDs, and insurance cards, for efficient processing.
• Prevent Fraud : Leverage advanced machine learning algorithms to detect fraudulent activities, including duplicate claims and document manipulation.
• Scalability : Efficiently handle large volumes of claims using multi-threading and batch processing to maintain system performance.
• Real-Time Processing :  Enable instant fraud detection and generate real-time reports for newly uploaded claims.

By implementing these features, the system ensured accuracy, security, and operational efficiency in fraud prevention and claim management.

Our Solution:

Our team of dedicated researcher and ML engineers developed a comprehensive, AI-driven fraud detection system tailored to the client’s needs. The solution incorporates advanced machine learning techniques, image processing, and API integrations to streamline claim verification and fraud prevention.

1. PDF Processing and Feature Extraction:

• PDF to Image Conversion: Convert PDF documents into high-resolution images for analysis.
• Perceptual Hashing: Generate unique hashes for images to detect duplicates.
• Deep Learning Models: Use MobileNetV2 for feature extraction and a custom-trained classifier to categorize images (e.g., bills, IDs).

2. Fraud Detection Workflow:

• Duplicate PDF Detection: Compare PDF hashes to identify exact duplicates.
• Image Similarity Analysis: Use cosine similarity and Structural Similarity Index (SSIM) to detect similar images.
• Cluster-Based Detection: Group similar images using PCA and Nearest Neighbors for efficient comparison.

3. Integration with External APIs:

• Claim Data Fetching: Fetch claim data and PDFs from external APIs.
• Hash Updates: Update the system with new claim hashes to maintain an up-to-date database.

4. Technologies Used:

• Backend: Python, Flask, cx_Oracle
• Machine Learning: PyTorch, TensorFlow, Scikit-learn
• Image Processing: OpenCV, PIL, pdf2image
• Database: Oracle
• APIs: RESTful API integration

Outcome:

The Automated Fraud Detection System effectively addressed the client’s requirements, delivering significant improvements:

• 30% reduction in fraudulent claims within the first three months.
• 40% increase in claim processing efficiency through automation.
• A scalable and secure solution capable of handling high volumes of claims seamlessly.

This system not only enhanced fraud detection but also optimized operational efficiency, providing long-term value to the client.