Claim Size Prediction

Claim size prediction in insurance aims to accurately estimate the cost of future claims, enabling more precise pricing and risk management. Recent research heavily utilizes machine learning, particularly tree-based ensemble methods (like random forests and gradient boosting) and neural networks, often incorporating both structured data (e.g., driver demographics, vehicle details) and unstructured data (e.g., claim descriptions) to improve prediction accuracy. These advancements address the inherent challenges of imbalanced datasets and the need for robust, interpretable models, ultimately leading to fairer and more efficient insurance practices. Furthermore, research explores mitigating bias in prediction models to ensure equitable pricing.