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UID:20250712T211404EDT-7442RgNewm@132.216.98.100
DTSTAMP:20250713T011404Z
DESCRIPTION:Non parametric individual claim reserving\n\nAccurate loss rese
 rves are an important item in the financial statement of an insurance comp
 any 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 propo
 sed parametric reserving models using underlying individual claims data ha
 s emerged. In this paper\, we introduce non parametric tools (machine lear
 ning mostly) to estimate outstanding and IBNR liabilities using covariable
 s available for each policy and policyholder and which may be informative 
 about claim frequency and severity as well as payments behaviors. This exe
 rcise is quite intricate and new since the target variable (claim severity
 ) is right-censored most of the time. The performance of our approach is e
 valuated 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 Chai
 n Ladder with respect to the bias and the volatility of the estimates.\n	\n
 	Joint work with Maximilien Baudry (DAMI Chair\, LSAF\, UCBL)\n
DTSTART:20171013T180000Z
DTEND:20171013T190000Z
LOCATION:Room CMT-2106\, CA\, Université Laval\, Pavillon Paul-Comtois
SUMMARY:Christian Robert\, ISFA\, Université Claude Bernard Lyon 1
URL:/mathstat/channels/event/christian-robert-isfa-uni
 versite-claude-bernard-lyon-1-272996
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