Individual Claims Forecasting with Bayesian Mixture Density Networks
This work addresses claims analytics for insurance professionals, but it appears incremental as it applies existing methods to a specific domain.
The paper tackles the problem of forecasting individual insurance claims by introducing a Bayesian mixture density network framework that integrates structured and unstructured data to produce multi-period cash flow forecasts and scenario analyses, with evaluation on publicly available data.
We introduce an individual claims forecasting framework utilizing Bayesian mixture density networks that can be used for claims analytics tasks such as case reserving and triaging. The proposed approach enables incorporating claims information from both structured and unstructured data sources, producing multi-period cash flow forecasts, and generating different scenarios of future payment patterns. We implement and evaluate the modeling framework using publicly available data.