The rising number of insurance claims has prompted insurance companies to detect fraudulent claims economically and effectively. This project aims to create a system based on machine learning that predicts insurance fraud from manual input as well as bulk Excel upload. The system uses three deep learning models to undertake three tasks, namely fraud detection, prediction of claim occurrence, and estimation of claim amount.The app is developed on Flask as a web framework and utilizes TensorFlow-based models trained on past insurance history. Two input modes are supported:1. Manual Mode: Users input four important features (age, policy premium, severity of incident, total claim value) through a web form.2. Excel Upload Mode: Users upload an Excel file with complete records to support batch processing using all features available.For making precise predictions, the data is preprocessed with Label Encoders and Scikit-learn scalers, with special handling for unknown or missing labels. The fraud detection model gives a binary classification (fraud or not), and the claim model gives a probability of a claim being filed. A regression model approximates the probable claim amount.The system has a user-friendly interface for individuals as well as insurance companies facilitating proactive fraud detection and improved risk management. This end-to-end pipeline showcases the real-world application of machine learning in automating and improving decision-making within the insurance sector.
Insurance fraud detection, machine learning, deep learning model, manual input, excel input, batch processing, Risk management, user friendly interface.
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