The DOI matches its Crossref record; 1 of the 53 references it cites were found in a registry or an index, and 52 are not checked yet.
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
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555
Nelder, J. A., & Wedderburn, R. W. M. (1972) · Journal of the Royal Statistical Society Series A (General) · doi:10.2307/2344614 · Cited by 7,749
Goldstein, A., Kapelner, A., Bleich, J., & Pitkin, E. (2015) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2014.907095 · Cited by 1,861
Volume loss from Antarctic ice shelves is accelerating
Paolo, F. S., Fricker, H. A., & Padman, L. (2015) · Science · doi:10.1126/science.aaa0940 · Cited by 945
The Quest for Artificial Intelligence
Nilsson, N. J. (2009) · Cambridge University Press eBooks · doi:10.1017/cbo9780511819346 · Cited by 896
Conversational processes and causal explanation.
Hilton, D. J. (1990) · Psychological Bulletin · doi:10.1037/0033-2909.107.1.65 · Cited by 456
Marked point process hotspot maps for homicide and gun crime prediction in Chicago
Mohler, G. (2014) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2014.01.004 · Cited by 190
Insurance Premium Prediction via Gradient Tree-Boosted Tweedie Compound Poisson Models
Yang, Y. A., Qian, W., & Zou, H. (2016) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2016.1200981 · Cited by 114
Cited by
Sorted by citations
Ladbury, C., Zarinshenas, R., Semwal, H., Tam, A., Vaidehi, N., Rodin, A. S., Liu, A., Glaser, S., Salgia, R., & Amini, A. (2022) · Translational Cancer Research · doi:10.21037/tcr-22-1626 · Cited by 86
Mullins, M., Holland, C. P., & Cunneen, M. (2021) · Patterns · doi:10.1016/j.patter.2021.100362 · Cited by 77
Machine Learning in P&C Insurance: A Review for Pricing and Reserving
Blier-Wong, C., Cossette, H., Lamontagne, L., & Marceau, É. (2020) · Risks · doi:10.3390/risks9010004 · Cited by 71
Personalizing Policies with AI: Improving Customer Experience and Risk Assessment
Rahul, N. (2023) · International Journal of Emerging Trends in Computer Science and Information Technology · doi:10.63282/3050-9246.ijetcsit-v4i1p110 · Cited by 54
Modelling Motor Insurance Claim Frequency and Severity Using Gradient Boosting
Clemente, C., Guerreiro, G. R., & Bravo, J. M. (2023) · Risks · doi:10.3390/risks11090163 · Cited by 28
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Towards Explainability of Machine Learning Models in Insurance Pricing. https://app.cochat.ai/papers/10.66573/001c.68374
- The paper
- Kuo, K., & Lupton, D. (2023). Towards Explainability of Machine Learning Models in Insurance Pricing. Variance. https://doi.org/10.66573/001c.68374
