Predictive Modeling for Insurance Claims Fraud Detection

Thesis Abstract

Abstract
Insurance fraud poses a significant challenge for insurance companies, leading to substantial financial losses and eroding public trust in the industry. To combat this issue, predictive modeling techniques offer a promising approach by leveraging data analytics to detect fraudulent insurance claims proactively. This thesis focuses on the development and implementation of a predictive modeling framework for insurance claims fraud detection. The research aims to enhance the accuracy and efficiency of fraud detection processes, thereby minimizing financial losses and improving the overall integrity of the insurance industry. The study begins with a comprehensive literature review to explore existing methodologies, technologies, and best practices in insurance fraud detection. Through an extensive review of relevant academic research and industry reports, this chapter provides a foundational understanding of the current landscape of insurance fraud and the challenges associated with detecting fraudulent claims. Building upon the insights gathered from the literature review, the research methodology chapter outlines the approach taken to develop and validate the predictive modeling framework. The methodology encompasses data collection, preprocessing, feature selection, model training, evaluation, and validation processes. By employing advanced machine learning algorithms such as logistic regression, decision trees, and neural networks, the study aims to build a robust fraud detection model capable of identifying suspicious patterns in insurance claims data. The findings chapter presents the results of the predictive modeling experiments conducted on a real-world insurance claims dataset. Through performance metrics such as accuracy, precision, recall, and F1 score, the effectiveness of the proposed fraud detection model is evaluated. The discussion delves into the strengths and limitations of the model, highlighting areas for further improvement and refinement. In conclusion, the study underscores the significance of predictive modeling in enhancing insurance claims fraud detection capabilities. By leveraging data-driven insights and machine learning algorithms, insurance companies can proactively identify and prevent fraudulent activities, safeguarding their financial resources and reputation. The thesis contributes to the ongoing efforts to combat insurance fraud and promote transparency within the industry. Recommendations for future research and practical implications are also discussed, emphasizing the continuous evolution of fraud detection techniques in the dynamic landscape of insurance operations.