Development of a Predictive Model for Insurance Claim Fraud Detection

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.