Actuarial Neural Network

Actuarial neural networks combine traditional actuarial models, like generalized linear models (GLMs), with neural networks to improve the accuracy and interpretability of insurance pricing and risk prediction. Current research focuses on enhancing model architectures, such as incorporating transformers and deep distribution regression methods, to better capture complex relationships within insurance data, including both cross-sectional and longitudinal data, and various feature types. This approach aims to leverage the strengths of both traditional statistical methods and the flexibility of deep learning, leading to more robust and reliable models for applications in non-life insurance pricing and risk management.