Using Machine Learning to Model Claims Experience and Reporting Delays for Pricing and Reserving

44 Pages Posted: 16 Oct 2019

See all articles by Louis Rossouw

Louis Rossouw

Gen Re - South Africa

Ronald Richman

QED Actuaries and Consultants

Date Written: October 7, 2019

Abstract

In this paper we review existing modelling approaches for analysing claims experience in the presence of reporting delays, reviewing the formulation of mortality incidence models such as GLMs. We then show how these approaches have traditionally been adjusted for late reporting of claims using either the IBNR approach or the more recent EBNER approach. We then go on to introduce a new model formulation that combines a model for late reported claims with a model for mortality incidence into a single model formulation. We then illustrate the use and performance of the traditional and the combined model formulations on data from a multinational reinsurer. We show how GLMs, lasso regression, gradient boosted trees and deep learning can be applied to the new formulation to produce results of superior accuracy compared to the traditional approaches.

Keywords: machine learning, IBNR, experience analysis, reinsurers

JEL Classification: G22

Suggested Citation

Rossouw, Louis and Richman, Ronald, Using Machine Learning to Model Claims Experience and Reporting Delays for Pricing and Reserving (October 7, 2019). Available at SSRN: https://ssrn.com/abstract=3465424 or http://dx.doi.org/10.2139/ssrn.3465424

Louis Rossouw (Contact Author)

Gen Re - South Africa ( email )

2nd floor, South Wing
Granger Bay Court, Beach Rd.
Cape Town, Western Cape 8001
South Africa
+27 21 412 7712 (Phone)

HOME PAGE: http://www.genre.com/knowledge/blog/contributors/louis-rossouw.html

Ronald Richman

QED Actuaries and Consultants ( email )

38 Wierda Road West
Sandton
Johannesburg, Gauteng 2196
South Africa

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