Harnessing Artificial Intelligence for Risk Assessment and Fraud Detection in Insurance: A Modern Approach to Predictive Modelling

Abstract This research investigates the potential of artificial intelligence (AI) to enhance two of the most critical processes in the insurance industry: risk assessment and fraud detection. As insurers face increasing pressure to process complex data, minimize losses, and provide more personalized services, traditional statistical models such as logistic regression often prove insufficient in capturing the nonlinear relationships and subtle patterns embedded in client behavior and claim history. The central research question addressed in this study is: How can artificial intelligence improve risk assessment and fraud detection in insurance compared to traditional statistical approaches? To answer this question, a simulation-based experimental methodology was employed. A synthetic dataset containing 10,000 insurance client profiles was generated, incorporating realistic demographic, behavioral, and policy-related variables. Several machine learning models were implemented, including supervised algorithms (Logistic Regression, Random Forest, XGBoost, Neural Networks, SVM) and unsupervised methods (Isolation Forest, Autoencoder, K-Means), with performance evaluated using accuracy, F1 score, precision, and recall. Model training and validation were conducted using cross-validation techniques and hyperparameter optimization to ensure robustness and generalizability. The results demonstrate a clear performance advantage of AI-based models over traditional statistical methods. XGBoost emerged as the best-performing model, achieving the highest accuracy (87.3%), F1 score (0.86), and strong precision and recall, confirming its robustness in both classification and detection tasks. Random Forest and Neural Networks also performed well, while Logistic Regression lagged significantly behind. Unsupervised models such as Isolation Forest and Autoencoders proved particularly useful in fraud detection, offering high recall rates suitable for anomaly detection and early-stage screening. The integration of multiple models into a hybrid AI architecture is recommended to balance precision with sensitivity. In conclusion, the findings of this research affirm that artificial intelligence provides a powerful and scalable solution for modern insurance analytics. AI not only improves the precision and efficiency of risk assessment and fraud detection but also enables dynamic, data-driven decision-making that traditional models cannot replicate. As the insurance industry becomes increasingly digitized, AI adoption represents not merely an opportunity but a strategic necessity for organizations seeking to optimize operations and maintain a competitive edge.