Thesis Abstract

**Abstract
** Fraudulent insurance claims pose a significant threat to the financial stability and integrity of insurance companies. Traditional fraud detection methods are often insufficient to effectively identify and prevent fraudulent activities, leading to substantial losses for insurers. This research investigates the application of machine learning algorithms for fraud detection in insurance claims to enhance the accuracy and efficiency of fraud detection processes. The study focuses on evaluating various machine learning algorithms, including decision trees, random forests, support vector machines, and neural networks, to determine their effectiveness in detecting fraudulent insurance claims. The research methodology involves collecting a comprehensive dataset of historical insurance claims, including both genuine and fraudulent cases, to train and test the machine learning models. Various performance metrics, such as accuracy, precision, recall, and F1 score, are utilized to evaluate the predictive capabilities of the algorithms in detecting fraudulent activities. Additionally, feature importance analysis is conducted to identify the most influential factors contributing to fraudulent claims. The findings of the study demonstrate that machine learning algorithms, particularly random forests and neural networks, outperform traditional fraud detection methods in accurately identifying fraudulent insurance claims. The results indicate that these algorithms can effectively detect complex patterns and anomalies indicative of fraudulent behavior, thereby enhancing the overall fraud detection capabilities of insurance companies. The implications of this research are significant for the insurance industry, as the adoption of machine learning algorithms can help insurers mitigate financial losses associated with fraudulent claims and improve operational efficiency. By leveraging advanced analytics and artificial intelligence technologies, insurance companies can proactively detect and prevent fraudulent activities, ultimately safeguarding their financial interests and enhancing customer trust. In conclusion, the analysis of machine learning algorithms for fraud detection in insurance claims offers a promising approach to combatting insurance fraud and enhancing the overall security of insurance operations. The research highlights the potential of artificial intelligence in transforming fraud detection processes and underscores the importance of leveraging innovative technologies to combat fraudulent activities effectively. This study contributes to the existing body of knowledge on fraud detection in insurance and provides practical insights for insurance companies seeking to enhance their fraud prevention strategies through advanced analytics and machine learning techniques.

Thesis Overview