Adjusting Manual Rates to Own Experience: Comparing the Credibility Approach to Machine Learning

Giorgio Alfredo Spedicato, Christophe Dutang () and Quentin Guibert ()
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Giorgio Alfredo Spedicato: Leitha SRL
Christophe Dutang: ASAR - Applied Statistics And Reliability - ASAR - LJK - Laboratoire Jean Kuntzmann - Inria - Institut National de Recherche en Informatique et en Automatique - CNRS - Centre National de la Recherche Scientifique - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes
Quentin Guibert: CEREMADE - CEntre de REcherches en MAthématiques de la DEcision - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique, LSAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon

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Abstract: Credibility theory is the usual framework in actuarial science when it comes to reinforcing individual experience by transfering rates estimated from collective information. Based on the paradigm of transfer learning, this article presents the idea that a machine learning (ML) model pre-trained using a rich market data porfolio can improve the prediction of rates for an individual insurance portfolio. This framework consists first in training several ML models on a market portfolio of insurance data. Pre-trained models provide valuable information on relations between features and predicted rates. Furthermore, features shared with the company dataset are used to predict rates better than the same ML models trained on the insurer's dataset alone. Our approach is illustrated with classical ML models on an anonymized dataset including both market data and data from an European non-life insurance company, and is compared with a hierarchical Bühlmann-Straub credibility model. We observe the transfert learning stragegy combining company data with external market data significantly improves the prediction accuracy compared to a ML model only trained on the insurer's data and provides competitive results compared to hierarchical credibility models.

Keywords: Transfer learning; Hierarchical credibility theory; Bühlmann credibility theory; Boosting; Deep Learning (search for similar items in EconPapers)
Date: 2023-03-15
Note: View the original document on HAL open archive server: https://hal.science/hal-04821310v1
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