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Towards Explainability of Machine Learning Models in Insurance Pricing
Open accessAbstract
Machine learning methods have garnered increasing interest among actuaries in recent years. However, their adoption by practitioners has been limited, partly due to the lack of transparency of these methods, as compared to generalized linear models. In this paper, we discuss the need for model interpretability in property & casualty insurance ratemaking, propose a framework for explaining models, and present a case study to illustrate the framework. Address for Correspondence: kevinykuo@gmail.com
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“A key requirement in the insurance (and financial) industry is the need for transparent and interpretable pricing models which are easily explainable to all stakeholders (e.g., managers, customers, shareholders, regulators, auditors), see, e.g., Kuo and Lupton (2023).”
From Modelling Motor Insurance Claim Frequency and Severity Using Gradient Boosting (2023) · cites this paper as Kuo and Lupton (2023)
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Metadata
- Authors
- Kuo, K., & Lupton, D.
- ORCID
- Kuo, Kevin
- Venue
- Variance
- Publisher
- Casualty Actuarial Society
- Published
- 2023-03-07
- Volume
- 16
- Issue
- 1
- ISSN
- 1940-6452 (ISSN-L)
- OpenAlex
- W3122287204
- Cited by
- 10
- References
- 50
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 2 · 2023: 1
- FWCI
- 0.44
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Variance (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Insurance and Financial Risk Management
- Social Sciences › Decision Sciences › Management Science and Operations Research › Probability and Risk Models
- Keywords
- explainability, model interpretability, insurance pricing, non-life insurance, ratemaking, model transparency
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