Deception Identification and Evaluation for Insurance Claims

Fraudulent insurance claims represent a significant threat to the financial stability of insurance companies and contribute to increased premiums for policyholders. With the growing volume of data in the insurance industry, the need for efficient and accurate fraud detection mechanisms has become more pressing. Machine learning (ML) offers powerful tools to detect anomalous patterns and identify potentially fraudulent claims. This study investigates the application of various machine learning algorithms to detect and analyze fraudulent insurance claims. Techniques such as Decision Trees, Random Forest, Support Vector Machines, and Gradient Boosting are explored to evaluate their effectiveness in identifying irregularities. The proposed system utilizes a structured dataset comprising historical claim information, including both legitimate and fraudulent cases. Feature selection methods are applied to enhance model accuracy and interpretability. Performance metrics such as accuracy, precision, recall, and F1-score are used to evaluate the models. The results demonstrate that machine learning can significantly enhance the detection of fraudulent claims, leading to cost savings and increased operational efficiency. Furthermore, the analysis provides insights into the most influential features contributing to fraudulent behavior, offering valuable support to insurers in decision-making processes. The integration of ML in fraud detection not only automates the identification process but also continuously improves with the influx of new data. This research supports the implementation of intelligent systems in the insurance sector to combat fraud effectively.