Frequency Severity Modeling
Frequency-severity modeling aims to predict both the frequency and severity of events, a crucial task across diverse fields like insurance, finance, and healthcare. Current research emphasizes developing robust models capable of handling complex data structures and dependencies between frequency and severity, employing techniques such as Bayesian CART models, deep learning architectures (including neural networks and Vision Transformers), and gradient-boosted trees. These advancements improve prediction accuracy and allow for more nuanced risk assessment, impacting areas such as insurance pricing, food security forecasting, and traffic incident management. Furthermore, research focuses on developing model-agnostic methods for reliable uncertainty quantification, enhancing the trustworthiness of predictions.