Actuarial Model

Actuarial modeling aims to predict future events, such as insurance claims, by statistically analyzing historical data. Current research emphasizes improving the accuracy and interpretability of these models, focusing on hybrid approaches that combine the strengths of traditional statistical methods (like Generalized Linear Models) with the flexibility of machine learning techniques such as neural networks (including transformers and multilayer perceptrons), gradient boosting machines, and autoencoders. This work addresses challenges like handling high-cardinality categorical data, zero-inflated distributions, and incorporating textual information (via NLP), ultimately leading to more robust and reliable risk assessments. The resulting advancements have significant implications for insurance pricing, reserving, and regulatory compliance.