Christian Robert, ISFA, Université Claude Bernard Lyon 1

Event

Room CMT-2106, Université Laval, Pavillon Paul-Comtois, CA

Non parametric individual claim reserving

Accurate loss reserves are an important item in the financial statement of an insurance company and are mostly evaluated by macro-level models with aggregate data in a run-off triangle. In recent years, a small set of literature that proposed parametric reserving models using underlying individual claims data has emerged. In this paper, we introduce non parametric tools (machine learning mostly) to estimate outstanding and IBNR liabilities using covariables available for each policy and policyholder and which may be informative about claim frequency and severity as well as payments behaviors. This exercise is quite intricate and new since the target variable (claim severity) is right-censored most of the time. The performance of our approach is evaluated by comparing the predictive values of the reserve estimates with their true values on a large number of simulated data. We also compare our individual approach with aggregated classical methods such as Mack's Chain Ladder with respect to the bias and the volatility of the estimates.

Joint work with Maximilien Baudry (DAMI Chair, LSAF, UCBL)

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