Project Abstract

Abstract
Insurance claim fraud is a pervasive issue that impacts the financial stability of insurance companies and raises costs for all policyholders. Detecting fraudulent claims efficiently and accurately is crucial in mitigating these negative effects. This research project focuses on the development and implementation of predictive modeling techniques for insurance claim fraud detection. By leveraging advanced data analytics and machine learning algorithms, the goal is to improve the detection of fraudulent claims and enhance the overall efficiency of fraud detection processes in the insurance industry. The project begins with a comprehensive review of existing literature on insurance claim fraud, predictive modeling, and machine learning applications in fraud detection. This review provides a solid foundation for understanding the current state of research in the field and identifying gaps that this study aims to address. In the research methodology chapter, the project outlines the data collection process, feature selection, model development, and evaluation metrics for the predictive modeling approach. Various machine learning algorithms, such as logistic regression, decision trees, random forest, and neural networks, will be explored and compared to determine which model performs best in detecting fraudulent insurance claims. The discussion of findings chapter presents the results of the predictive modeling experiments and evaluates the performance of different algorithms in terms of accuracy, sensitivity, specificity, and other relevant metrics. The analysis will also include a comparison of the computational efficiency and scalability of the models to assess their practical applicability in real-world insurance claim fraud detection scenarios. The conclusion and summary chapter provide a synthesis of the research findings, highlighting the effectiveness of predictive modeling in detecting insurance claim fraud. The study concludes with recommendations for insurance companies to implement these advanced analytics techniques to strengthen their fraud detection capabilities and minimize financial losses due to fraudulent claims. Overall, this research project contributes to the growing body of knowledge on fraud detection in the insurance industry and offers practical insights for improving fraud detection processes through the application of predictive modeling techniques. By leveraging data-driven approaches, insurance companies can enhance their ability to identify and prevent fraudulent activities, ultimately benefiting both the industry and policyholders.

Project Overview