Boosting Insights in Insurance Tariff Plans with Tree-Based Machine Learning Methods-Reference-Cited by-同舟云学术

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Boosting Insights in Insurance Tariff Plans with Tree-Based Machine Learning Methods

Published:2020-07-13 Issue:2 Volume:25 Page:255-285
ISSN:1092-0277
Container-title:North American Actuarial Journal
language:en
Short-container-title:North American Actuarial Journal

Author:

Henckaerts Roel¹²,Côté Marie-Pier³,Antonio Katrien¹²⁴,Verbelen Roel¹²

Affiliation:

1. Faculty of Economics and Business, KU Leuven, Leuven, Belgium

2. LRisk, Leuven Research Center on Insurance and Financial Risk Analysis, KU Leuven, Leuven, Belgium

3. Ecole d’actuariat, Université Laval, Québec, Québec, Canada

4. Faculty of Economics and Business, University of Amsterdam, Amsterdam, The Netherlands

Funder

Society of Actuaries James C. Hickman Scholar Program

Ageas Research Chair

KU Leuven’s research council

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Economics and Econometrics,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/10920277.2020.1745656

Reference58 articles.

1. A Multilevel Analysis of Intercompany Claim Counts

2. Bagging predictors

3. Regression Trees Identify Relevant Interactions: Can This Improve the Predictive Performance of Risk Adjustment?

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4. Bagging Vs. Boosting in Ensemble Machine Learning? An Integrated Application to Fraud Risk Analysis in the Insurance Sector;Applied Artificial Intelligence;2024-05-20

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