An Individual Claims Reserving Model for Reported Claims

28 Pages Posted: 22 Jun 2020

See all articles by Andrea Gabrielli

Andrea Gabrielli

ETH Zurich, Department of Mathematics, RiskLab, Students

Date Written: May 28, 2020

Abstract

We present a claims reserving technique that uses claim-specific feature and past payment information in order to estimate claims reserves for individual reported claims. We design one single neural network allowing us to estimate expected future cash flows for every individual reported claim. We introduce a consistent way of using dropout layers in order to fit the neural network to the incomplete time series of past individual claims payments. A proof of concept is provided by applying this model to a data set for which the true outstanding payments for reported claims are known.

Keywords: Claims Reserving, Individual Claims, RBNS Reserves, Neural Networks, Multi-Task Learning, Dropout, Time Series, Micro Reserving

JEL Classification: G22, C02, C13, C15, C45, C50, C51, C52, C53

Suggested Citation

Gabrielli, Andrea, An Individual Claims Reserving Model for Reported Claims (May 28, 2020). Available at SSRN: https://ssrn.com/abstract=3612930 or http://dx.doi.org/10.2139/ssrn.3612930

Andrea Gabrielli (Contact Author)

ETH Zurich, Department of Mathematics, RiskLab, Students ( email )

Switzerland

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
348
Abstract Views
1,007
Rank
157,801
PlumX Metrics