Self-Assembling Insurance Claim Models Using Regularized Regression and Machine Learning

40 Pages Posted: 15 Sep 2018

See all articles by Gráinne McGuire

Gráinne McGuire

Taylor Fry

Greg Taylor

UNSW Australia Business School, School of Risk & Actuarial Studies

Hugh Miller

Taylor Fry

Date Written: August 31, 2018

Abstract

The lasso is applied in an attempt to automate the loss reserving problem. The regression form contained within the lasso is a GLM, and so that the model has all the versatility of that type of model, but the model selection is automated and the parameter coefficients for selected terms will not be the same.

There are two applications presented, one to synthetic data in conventional triangular form, and another to real data.The secret of success in such an endeavor is the selection of the set of candidate basis functions for representation of the data set. Cross-validation is used for model selection.

The lasso performs well in modelling, identifying known features in the synthetic data, and tracking them accurately. This is despite complexity in those features that would challenge, and possibly defeat, most loss reserving alternatives. In the case of real data, the lasso also succeeds in tracking features of the data that analysis of the data set over many years has rendered virtually known.

A later section of the paper discusses the prediction error associated with a lasso-based loss reserve. It is seen that the procedure can be readily adapted to the estimation of parameter and process error, but can also estimate one component of model error. To the authors knowledge, no other loss reserving model in the literature does so.

Keywords: Bootstrap, Cross-Validation, GLM, Feature Selection, Lasso, Loss Reserving, Machine Learning, Regularized Regression

JEL Classification: C13, J22

Suggested Citation

McGuire, Gráinne and Taylor, Greg and Miller, Hugh, Self-Assembling Insurance Claim Models Using Regularized Regression and Machine Learning (August 31, 2018). Available at SSRN: https://ssrn.com/abstract=3241906 or http://dx.doi.org/10.2139/ssrn.3241906

Gráinne McGuire

Taylor Fry

55 Clarence Street
Sydney
Australia

Greg Taylor (Contact Author)

UNSW Australia Business School, School of Risk & Actuarial Studies ( email )

Level 6, East Lobby
UNSW Business School Building, UNSW
Sydney, NSW 2052
Australia
+61 421 338 448 (Phone)

Hugh Miller

Taylor Fry ( email )

55 Clarence Street
Sydney
Australia

Register to save articles to
your library

Register

Paper statistics

Downloads
146
Abstract Views
489
rank
197,252
PlumX Metrics