Author : R.Senthilkumar
Page No: 1 - 19
Abstract : Fraudulent activities in insurance claims have become a significant challenge for the insurance industry, leading to substantial financial losses annually. This project, titled "Fraud Detection and Analysis for Insurance Claim using Machine Learning," aims to develop a robust and efficient system to identify and analyze fraudulent claims. The system leverages machine learning techniques to analyze patterns, anomalies, and inconsistencies in claim data, enabling early detection of potentially fraudulent activities. Key features of this system include data preprocessing to handle missing or inconsistent information, feature selection to identify critical indicators of fraud, and model training using algorithms such as Random Forest, Logistic Regression, and Gradient Boosting. The model is trained on historical claim data to achieve high accuracy in distinguishing fraudulent claims from legitimate ones. Performance evaluation metrics such as accuracy, precision, recall, and F1-score are employed to assess the system's effectiveness. Additionally, the project incorporates advanced techniques like Natural Language Processing (NLP) to analyze claim narratives and identify suspicious patterns. Visualization tools are also integrated to provide insights into the nature of detected fraud and enhance decision-making for insurance analysts.The proposed solution not only minimizes financial losses but also improves the operational efficiency of insurance companies by automating fraud detection processes. T
Keyword Keywords: Insurance Fraud Detection, Machine Learning, Fraudulent Claims Analysis