Claim Frequency

Claim frequency modeling in insurance aims to accurately predict the number of claims an individual or entity will file, crucial for setting fair premiums and managing risk. Current research emphasizes advanced statistical models, including zero-inflated variations of generalized linear models and boosted tree methods like CatBoost, to handle the high frequency of zero claims and the complexities of diverse data sources (e.g., telematics, textual descriptions). Furthermore, federated learning techniques are being explored to leverage data from multiple insurers while preserving privacy. These improvements in modeling accuracy and data utilization have significant implications for insurance pricing, risk management, and broader applications in other fields dealing with imbalanced datasets and privacy concerns.