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Activity Number: 435 - SPEED: Sports to Fire: Fascinating Applications of Statistics
Type: Contributed
Date/Time: Tuesday, July 31, 2018 : 3:05 PM to 3:50 PM
Sponsor: Section on Risk Analysis
Abstract #332829
Title: Claim-Level Models Using Statistical Learning Techniques and Risk Analysis
Author(s): Mathieu Pigeon* and Francis Duval
Companies: Université du Québec à Montréal and Université du Québec à Montréal
Keywords: Predictive modelling; Loss reserving; Statistical learning; Risk Analysis; Individual dataset

Modeling based on data information is one of the most challenging research topics in actuarial science for loss reserving and risk valuation. Most of these analyses are based on aggregated data but nowadays it is clear that this approach does not tell the whole story about a claim and does not describe precisely its development. This talk will describe the rationale of the use of claim-level information in combination with statistical learning techniques to demonstrate why it is important and illustrate results using real data examples.

In our study, we compare traditional aggregated techniques (portfolio-level) with individual models (claim-level) using information about each of the payments made for each of the claims in the portfolio, as well as characteristics of the insured. Model performance is evaluated using out-of-sample mean squared error of prediction and mean absolute error based on a detailed dataset from a P & C insurance company. Predictions for future total loss and its distribution, as well as for standard risk measures such as Value-at-Risk and Tail Conditional Expectation, are evaluated and discussed using simulations.

Authors who are presenting talks have a * after their name.

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