Thesis Abstract

Abstract
Fraudulent insurance claims present a significant challenge for insurance companies, leading to financial losses and eroding customer trust. The use of machine learning techniques for fraud detection has shown promise in improving the accuracy and efficiency of fraud detection processes. This thesis investigates the application of predictive modeling using machine learning algorithms for insurance claim fraud detection. The primary objective is to develop a model that can effectively detect fraudulent insurance claims, thereby enabling insurance companies to mitigate financial losses and enhance their fraud detection capabilities. The research begins with a comprehensive review of existing literature on fraud detection in the insurance industry, focusing on the challenges and opportunities associated with using machine learning for fraud detection. The literature review highlights the importance of data quality, feature selection, and model evaluation in developing effective fraud detection models. The research methodology section outlines the process of collecting and preprocessing insurance claim data, selecting relevant features, and building and evaluating machine learning models for fraud detection. Various machine learning algorithms, including logistic regression, random forest, and support vector machines, are applied and compared to identify the most effective model for fraud detection. The findings chapter presents the results of the experiment, including the performance metrics of the different machine learning models in detecting fraudulent insurance claims. The discussion delves into the strengths and limitations of each model, identifying areas for improvement and potential future research directions. In conclusion, the study demonstrates the potential of machine learning techniques in improving insurance claim fraud detection. The developed predictive modeling approach shows promise in effectively identifying fraudulent claims, thereby helping insurance companies to reduce financial losses and enhance their fraud detection capabilities. The study contributes to the existing body of knowledge on fraud detection in the insurance industry and provides practical insights for implementing machine learning solutions in fraud detection processes.

Thesis Overview