From point to probabilistic gradient boosting for claim frequency and severity prediction
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| Tag | 1 | 2 | Value |
|---|---|---|---|
| LDR | 00000cab a2200000 4500 | ||
| 001 | MAP20260003023 | ||
| 003 | MAP | ||
| 005 | 20260211184311.0 | ||
| 008 | 260206e20250811che|||p |0|||b|eng d | ||
| 040 | $aMAP$bspa$dMAP | ||
| 084 | $a6 | ||
| 100 | $0MAPA20260002408$aChevalier, Dominik | ||
| 245 | 1 | 0 | $aFrom point to probabilistic gradient boosting for claim frequency and severity prediction$cDominik Chevalier and Marie-Pier Côté |
| 520 | $aThis survey paper provides a comprehensive review of point and probabilistic gradient boosting algorithms applied to insurance claim frequency and severity modelling. The authors compare leading boosting methodssuch as GBM, XGBoost, LightGBM, CatBoost, EGBM, XGBoostLSS, cyc-GBM and NGBoostusing public insurance datasets of varying size and complexity. The study evaluates computational efficiency, predictive performance, model adequacy, and calibration. Results show that LightGBM and XGBoostLSS are the most computationally efficient, CatBoost performs well with high-cardinality categorical variables, and probabilistic boosting methods can improve model adequacy without sacrificing accuracy. The paper highlights that no single method is universally superior, and offers actuarial guidance for choosing boosting algorithms for pricing and risk modelling | ||
| 650 | 4 | $0MAPA20080592011$aModelos actuariales | |
| 650 | 4 | $0MAPA20230006092$aFrecuencia siniestral | |
| 650 | 4 | $0MAPA20170005476$aMachine learning | |
| 650 | 4 | $0MAPA20080611613$aModelos probabílisticos | |
| 650 | 4 | $0MAPA20080564322$aTarificación | |
| 650 | 4 | $0MAPA20080592059$aModelos predictivos | |
| 700 | 1 | $0MAPA20260002415$aCôté, Marie-Pier | |
| 710 | 2 | $0MAPA20180008764$aSpringer | |
| 773 | 0 | $wMAP20220007085$g11/08/2025 Volume 15 - Number 2 - August 2025 $tEuropean Actuarial Journal$dCham, Switzerland : Springer Nature Switzerland AG, 2021-2022 |