Collective reserving using individual claims data

The aim of this paper is to operationalize claims reserving based on individual data. We design a modeling architecture that six different neural networks. Each network separate module serves certain purpose. apply our data and predict their settlement processes monthly time grid. A proof concept provided by benchmarking the resulting reserves with ones received from classical chain-ladder method which uses much coarser (aggregated)