  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.
