Thesis Abstract

Abstract
Insurance fraud is a significant challenge faced by insurance companies, leading to financial losses and increased premiums for policyholders. To combat this issue, predictive modeling techniques have emerged as a powerful tool for detecting fraudulent insurance claims. This thesis focuses on the development and implementation of a predictive modeling system for insurance claim fraud detection. The research begins with a comprehensive review of the existing literature on insurance fraud, predictive modeling, and fraud detection techniques. Building on this foundation, the thesis presents a detailed methodology for developing and evaluating predictive models for fraud detection in insurance claims. The research methodology includes data collection from insurance companies, feature selection, data preprocessing, model training, evaluation, and validation. Various machine learning algorithms, such as logistic regression, decision trees, random forest, and neural networks, are employed to build predictive models that can effectively identify fraudulent insurance claims. The findings of the study demonstrate the effectiveness of predictive modeling in detecting fraudulent insurance claims. The developed models exhibit high accuracy, sensitivity, and specificity in identifying potentially fraudulent claims, thereby helping insurance companies reduce financial losses and improve overall operational efficiency. The discussion of the findings highlights the key insights gained from the research, including the importance of data quality, feature selection, model performance evaluation, and the potential challenges of implementing predictive modeling systems in real-world insurance settings. In conclusion, this thesis contributes to the field of insurance fraud detection by providing a detailed framework for developing predictive models and demonstrating their effectiveness in detecting fraudulent insurance claims. The study underscores the significance of leveraging advanced analytics and machine learning techniques to combat insurance fraud and protect the interests of both insurance companies and policyholders. Overall, the research presented in this thesis offers valuable insights and practical recommendations for insurance companies seeking to enhance their fraud detection capabilities through the use of predictive modeling technologies. By adopting these approaches, insurance companies can proactively identify and prevent fraudulent activities, thereby safeguarding their financial resources and maintaining trust with their policyholders.

Thesis Overview