Thesis Abstract

Abstract
Fraudulent insurance claims pose a significant challenge to insurance companies, leading to financial losses and undermining the trust of policyholders. In response to this issue, the development of predictive models for fraud detection has gained importance in the insurance industry. This thesis focuses on the development of a predictive model specifically tailored for the detection of insurance claim fraud. The aim of this research is to enhance fraud detection capabilities, thereby enabling insurance companies to mitigate risks associated with fraudulent activities. The study begins with a comprehensive review of existing literature on insurance claim fraud, fraud detection techniques, and predictive modeling in the insurance sector. By examining previous research, this thesis establishes a solid foundation for the development of an effective predictive model for fraud detection in insurance claims. The research methodology chapter outlines the approach taken to design and implement the predictive model. Various methodologies, including data collection, data preprocessing, feature selection, model building, and evaluation, are discussed in detail. The selection of appropriate algorithms and techniques for building the predictive model is crucial to ensuring its accuracy and reliability. The findings chapter presents the results of applying the developed predictive model to real-world insurance claim data. The effectiveness and performance of the model in detecting fraudulent claims are analyzed and discussed. The chapter also highlights the key insights gained from the findings and their implications for fraud detection practices in the insurance industry. In conclusion, this thesis contributes to the field of insurance claim fraud detection by proposing a novel predictive model that leverages advanced data analytics and machine learning techniques. The developed model demonstrates promising results in identifying potentially fraudulent claims, thereby assisting insurance companies in preventing financial losses and safeguarding the integrity of their operations. Overall, this research provides valuable insights and recommendations for improving fraud detection strategies in the insurance sector. Keywords Insurance claim fraud, Predictive modeling, Fraud detection, Data analytics, Machine learning, Risk mitigation.

Thesis Overview

The project titled "Development of a Predictive Model for Insurance Claim Fraud Detection" aims to address the critical issue of insurance claim fraud through the implementation of advanced predictive modeling techniques. Insurance claim fraud poses a significant challenge for insurance companies, leading to financial losses and undermining the integrity of the insurance industry. By developing a predictive model specifically designed to detect fraudulent insurance claims, this research seeks to enhance fraud detection capabilities and improve overall risk management strategies within the insurance sector. The research will begin with a comprehensive review of existing literature on insurance claim fraud detection, exploring current methodologies, challenges, and opportunities for improvement. This literature review will provide a solid foundation for understanding the complexities of insurance fraud and the potential applications of predictive modeling in this context. Following the literature review, the research will focus on the methodology for developing the predictive model. This will involve collecting and analyzing historical insurance claim data, identifying patterns and anomalies associated with fraudulent claims, and selecting appropriate variables and algorithms for the predictive model. The research will also explore the use of machine learning and data mining techniques to enhance the accuracy and efficiency of the predictive model. The findings of the research will be presented and discussed in detail, highlighting the effectiveness of the developed predictive model in detecting insurance claim fraud. The discussion will also address any limitations or challenges encountered during the research process and provide recommendations for future research and implementation. In conclusion, the project "Development of a Predictive Model for Insurance Claim Fraud Detection" represents a significant contribution to the field of insurance fraud detection by leveraging advanced predictive modeling techniques to enhance fraud detection capabilities. This research has the potential to benefit insurance companies by helping them identify and mitigate fraudulent activities, ultimately leading to improved risk management practices and increased trust in the insurance industry.