Thesis Abstract

Abstract
Insurance claim fraud is a significant issue that impact both insurance companies and policyholders. Detecting fraudulent claims is crucial to prevent financial losses and maintain the integrity of the insurance industry. This research aims to develop a predictive modeling approach for insurance claim fraud detection using advanced machine learning techniques. The study focuses on leveraging historical claims data to build a model that can accurately predict the likelihood of a claim being fraudulent. The thesis begins with an introduction that outlines the importance of fraud detection in the insurance sector. The background of the study provides an overview of the current state of insurance claim fraud and the challenges associated with detecting fraudulent activities. The problem statement highlights the need for more effective fraud detection methods to combat increasingly sophisticated fraud schemes. The objectives of the study are to develop a predictive model that can accurately identify fraudulent insurance claims, assess the performance of the model using real-world data, and provide recommendations for implementing fraud detection systems in insurance companies. The limitations of the study, such as data availability and model complexity, are also discussed, along with the scope of the research, which focuses on property and casualty insurance claims. The significance of the study lies in its potential to improve fraud detection processes in the insurance industry, leading to cost savings for insurance companies and fairer premiums for policyholders. The structure of the thesis is outlined to guide readers through the research methodology, literature review, discussion of findings, and conclusion. The literature review explores existing research on insurance claim fraud detection, machine learning algorithms, and predictive modeling techniques. Key themes include feature selection, model evaluation metrics, and the importance of data quality in fraud detection. The research methodology section describes the data collection process, feature engineering techniques, model training and evaluation procedures, and validation methods. The discussion of findings presents the results of the predictive modeling approach, including model performance metrics, feature importance analysis, and potential challenges in implementation. The conclusion summarizes the key findings of the study, highlights the contributions to the field of insurance fraud detection, and offers recommendations for future research and practical applications. In conclusion, this thesis provides a comprehensive analysis of predictive modeling for insurance claim fraud detection, offering valuable insights for insurance companies, policymakers, and researchers. By developing and evaluating a predictive model for fraud detection, this research contributes to the ongoing efforts to combat insurance claim fraud and protect the interests of both insurers and policyholders.

Thesis Overview

The project titled "Predictive Modeling for Insurance Claim Fraud Detection" focuses on the development and implementation of predictive modeling techniques to enhance the detection of fraudulent insurance claims. Insurance fraud is a significant issue that impacts both insurance companies and policyholders, leading to financial losses and increased premiums. Traditional methods of fraud detection are often reactive and labor-intensive, resulting in delayed identification of fraudulent activities. Therefore, the utilization of predictive modeling offers a proactive approach to fraud detection by analyzing historical data and identifying patterns indicative of fraudulent behavior. The research aims to address the limitations of current fraud detection methods by leveraging advanced predictive modeling algorithms to enhance the accuracy and efficiency of identifying fraudulent insurance claims. By analyzing historical claim data, the project seeks to develop predictive models that can accurately predict the likelihood of a claim being fraudulent based on various attributes and patterns. These models will be trained using machine learning algorithms such as logistic regression, decision trees, random forests, and neural networks to classify claims as either fraudulent or legitimate. The project will also explore the integration of data preprocessing techniques, feature selection methods, and model evaluation metrics to optimize the performance of the predictive models. By selecting relevant features and fine-tuning model parameters, the research aims to improve the accuracy, sensitivity, and specificity of the fraud detection models. Additionally, the project will investigate the interpretability of the models to provide insights into the factors contributing to fraudulent claims and facilitate decision-making for insurance investigators. Furthermore, the research methodology will involve data collection from insurance claim databases, data preprocessing, feature engineering, model training, evaluation, and validation using historical claim data. The project will analyze the performance of the predictive models in terms of accuracy, precision, recall, and F1 score to assess their effectiveness in detecting fraudulent claims. The findings of the research will be presented through a detailed discussion of the results, highlighting the strengths and limitations of the predictive modeling approach in fraud detection. In conclusion, the project "Predictive Modeling for Insurance Claim Fraud Detection" aims to contribute to the improvement of fraud detection processes in the insurance industry by harnessing the power of predictive modeling techniques. By developing accurate and efficient fraud detection models, the research seeks to assist insurance companies in mitigating financial losses, enhancing operational efficiency, and protecting the interests of policyholders.