Fair and Explainable AI for Auto Insurance Risk Assessment
Keywords:
Auto insurance underwriting, Explainable AI, explainable machine learning, fairness, risk assessment, explainable predictions, predictive performance.Abstract
Auto insurance underwriting aims to assess applicants' risk and set appropriate insurance premiums. Beyond accurate predictions, underwriting models must satisfy additional requirements to support transparency and fairness—increasingly relevant issues in light of regulatory scrutiny. Should risk assessment help customers better understand the potential for damage or loss, explainable artificial intelligence (XAI) methods are a promising way to enable stakeholders to transparently grasp the model's underlying rationale. The proposed explanatory approach focuses on comparing the outcomes of commonly used predictive models (XGBoost, logistic regression, random forests, support vector classification, and multilayer perceptrons) with those of well-known XAI methods (LIME and SHAP) to assess the trade-off between predictive performance and interpretability. Classification fairness is evaluated across sensitive demographic attributes: age, gender, marital status, and zip code. Applying fairness definitions from the literature highlights cases of unequal treatment for sub-demographic groups.
Results demonstrate that the best-performing non-explainable machine learning model for risk assessment—in this case, an XGBoost model—offers the least predictive capability considering the fairness trade-off when compared with its LIME and SHAP explanations. Nevertheless, these XAI techniques present their own disadvantageous trade-offs—namely, an overall drop in performance, predictive quality, and decision-making capability—compared with simpler, yet inherently more interpretable models such as logistic regression and support vector classification. Addressing such trade-offs demands caution and context consideration, yet the application of XAI techniques to support explanations to non-designated recipients remains advantageous, especially in modeling domains in which consumer protection and risk understanding are desired outcomes.
References
1. Aas, K., Charpentier, A., Huang, F., & Richman, R. (2024). Insurance analytics: Prediction, explainability, and fairness. Annals of Actuarial Science, 18(3), 678–704.
2. Ali, S., Abuhmed, T., El-Sappagh, S., Muhammad, K., Alonso-Moral, J. M., Confalonieri, R., Guidotti, R., Del Ser, J., Díaz-Rodríguez, N., & Herrera, F. (2023). Explainable artificial intelligence (XAI): What we know and what is left to attain trustworthy artificial intelligence. Information Fusion, 99, 101805.
3. Baumann, J., & Loi, M. (2023). Fairness and risk: An ethical argument for a group fairness definition insurers can use. Philosophy & Technology, 36, 45.
4. Caton, S., & Haas, C. (2024). Fairness in machine learning: A survey. ACM Computing Surveys, 56(7), 1–38.
5. Chen, R., Sun, L., & Chen, Y. (2023). Explainable artificial intelligence for risk prediction: A systematic review. Artificial Intelligence Review, 56, 11291–11321.
6. Mangalampalli, B. M. (2024). Transparent Intelligence Explainability Frameworks for AI-Driven Clinical Decision Support in Healthcare Business Intelligence. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(3), 10566-10579.
7. Chouldechova, A., & Roth, A. (2021). A snapshot of the frontiers of fairness in machine learning. Communications of the ACM, 64(5), 82–89.
8. Deng, W., Li, X., & Wang, H. (2022). Interpretable machine learning for risk prediction and decision-making: A review. Expert Systems with Applications, 198, 116785.
9. Doshi-Velez, F., & Kim, B. (2021). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.
10. Mattaparthi, R. (2024). Transformer-Based Fault Diagnosis for Large-Scale Standby Power Generators: Partial Discharge Pattern Recognition at Hyperscale Data Center Installations. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 8781-8799.
11. European Insurance and Occupational Pensions Authority. (2021). Artificial intelligence governance principles: Towards ethical and trustworthy AI in insurance. EIOPA.
12. European Insurance and Occupational Pensions Authority. (2024). Opinion on artificial intelligence governance and risk management. EIOPA.
13. Fanaee-T, H., & Thabtah, F. (2022). Machine learning interpretability and explainability in insurance applications. Journal of Risk and Financial Management, 15(8), 354.
14. Loganathan, R., & Kolla, S. H. (2024). Harnessing generative AI for adaptive risk policy generation in multi-regulatory data center environments. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 15(3), 505-515.
15. Ferrara, E. (2023). Fairness and bias in artificial intelligence: A brief survey of sources, impacts, and mitigation strategies. Science, 6(1), 3.
16. Guidotti, R. (2022). Counterfactual explanations and their applications in machine learning. ACM Computing Surveys, 54(8), 1–35.
17. Hünermund, P., & Bareinboim, E. (2021). Causal inference and data fusion in econometrics and machine learning. Econometrics Journal, 24(1), 1–23.
18. Kuo, K., & Lupton, D. (2023). Towards explainability of machine learning models in insurance pricing. Variance, 16(1), 1–22.
19. Reddy, V. A. R. (2024). Generative Intelligence for Healthcare Claims Processing and Personalized Benefits Management. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 7(3), 14099.
20. Kshirsagar, R., Hsu, L.-Y., Greenberg, C. H., McClelland, M., Mohan, A., Shende, W., & Alvarado, M. (2021). Accurate and interpretable machine learning for transparent pricing of health insurance plans. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 15127–15136.
21. Loi, M., & Christen, M. (2021). Choosing how to discriminate: Navigating ethical trade-offs in fair algorithmic design for the insurance sector. Philosophy & Technology, 34, 967–992.
22. Lindholm, M., Richman, R., Tsanakas, A., & Wüthrich, M. V. (2024). What is fair? Proxy discrimination versus demographic disparities in insurance pricing. ASTIN Bulletin, 54(2), 363–392.
23. Mangala, N. (2022). Real-Time Data Quality Monitoring and Gating Frameworks in Cloud-Based Data Pipelines. International Journal of Research and Applied Innovations, 5(6), 8197-8219.
24. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), Article 115.
25. Molnar, C. (2022). Interpretable machine learning: A guide for making black box models explainable (2nd ed.). Independently published.
26. Pawelczyk, M., Broelemann, K., & Kasneci, G. (2021). Learning model-agnostic counterfactual explanations for tabular data. Proceedings of the Web Conference 2021, 3126–3137.
27. Pessach, D., & Shmueli, E. (2021). A review on fairness in machine learning. ACM Computing Surveys, 54(6), Article 129.
28. Ribeiro, M. T., Singh, S., & Guestrin, C. (2021). Model-agnostic interpretability of machine learning models: Methods and applications. Communications of the ACM, 64(5), 56–63.
29. Kolla, S. K., & Mangalampalli, B. M. (2024). Edge-Based Deep Learning Systems for Point-of-Care Diagnostic Intelligence. Journal of Neonatal Surgery, 13(1), 2387-2399.
30. Rudin, C., Chen, C., Chen, Z., Huang, H., Semenova, L., & Zhong, C. (2022). Interpretable machine learning: Fundamental principles and 10 grand challenges. Statistics Surveys, 16, 1–85.
31. Samek, W., Montavon, G., Lapuschkin, S., Anders, C. J., & Müller, K.-R. (2021). Explaining deep neural networks and beyond: A review of methods and applications. Proceedings of the IEEE, 109(3), 247–278.
32. Slack, D., Hilgard, S., Jia, E., Singh, S., & Lakkaraju, H. (2021). Counterfactual explanations can be manipulated. Advances in Neural Information Processing Systems, 34, 26590–26601.
33. Smith, L. T., Pirchalski, E., & Golbin, I. (2022). Avoiding unfair bias in insurance applications of AI models. Society of Actuaries Research Institute.
34. Mahadevan, S. (2024). Clouding the Future: Innovating Towards Net-Zero Emissions. International Journal of Computing and Engineering, 6(2), 17-23.
35. Suresh, H., & Guttag, J. V. (2021). A framework for understanding sources of harm throughout the machine learning life cycle. Proceedings of the 2021 ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization, 1–9.
36. Verma, S., & Rubin, J. (2021). Fairness definitions explained. Proceedings of the International Workshop on Software Fairness, 1–7.
37. Wolsztynski, E. (2024). Fair insurance pricing and the role of machine learning. Insurance: Mathematics and Economics, 118, 1–15.
38. Xie, S. (2021). Improving explainability of major risk factors in artificial neural networks for auto insurance rate regulation. Risks, 9(7), 126.
39. Yang, K., Qin, X., & Zhang, Y. (2022). Explainable machine learning for insurance risk prediction and pricing. Risks, 10(9), 176.
40. Kolla, S. H., & Peddi, R. K. (2024). Designing Governance-Aligned GenAI Pipelines Using Small Language Models for Enterprise Workflow Intelligence. International Journal of Science, Research and Technology, 7(6), 13256-13268.
41. Zafar, M. B., Valera, I., Rogriguez, M. G., & Gummadi, K. P. (2021). Fairness constraints: Mechanisms for fair classification. ACM Computing Surveys, 54(4), 1–35.
42. Zhou, Z., Chen, X., & Wang, J. (2023). Explainable artificial intelligence in financial and insurance risk management: A systematic review. Journal of Risk and Financial Management, 16(5), 244.
43. Zliobaite, I. (2022). Measuring discrimination in algorithmic decision making. Data Mining and Knowledge Discovery, 36, 1–35.
44. Yandamuri, U. S. (2024). AI-Driven Decision Support Systems for Operational Optimization in Hospitality Technology. Metallurgical and Materials Engineering.
Additional Files
Published
Issue
Section
License
Copyright (c) 2025 Luca Bianchi (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The American Online Journal of Science and Engineering (AOJSE) is an open-access journal, and all published articles are made freely available to readers worldwide under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license governs the use, sharing, adaptation, and distribution of all content published in AOJSE and is designed to promote the broadest possible dissemination and reuse of scholarly research.
Creative Commons Attribution 4.0 International License (CC BY 4.0)
Under the Creative Commons Attribution 4.0 International License, any user is free to share, copy, and redistribute the published material in any medium or format, and to adapt, remix, transform, and build upon the material for any purpose, including commercial use, provided that appropriate credit is given to the original authors and source. The license cannot be revoked as long as the terms are followed.
When using or redistributing content published in AOJSE, users must provide proper attribution by citing the original authors' names, the article title, the journal name (AOJSE), the volume and issue number, the year of publication, and the DOI or URL of the article. Users must also indicate if any changes were made to the original work. Attribution must be provided in a reasonable manner but must not suggest that the authors or AOJSE endorse the user or their use of the material.
Author Rights and Copyright Retention
AOJSE respects and upholds the intellectual property rights of all contributing authors. Authors who publish in AOJSE retain full copyright over their work. By submitting a manuscript to AOJSE, authors grant the journal a non-exclusive, worldwide, royalty-free license to publish, reproduce, distribute, publicly display, and archive the article in print and electronic formats. This license allows AOJSE to make the article freely accessible to readers globally while ensuring that the authors remain the rightful owners of their work.
Authors are permitted to deposit their published articles in institutional repositories, personal websites, academic networking platforms, and preprint servers, provided that the original publication in AOJSE is acknowledged and properly cited. Authors may also reuse their published content in subsequent works, presentations, teaching materials, and grant applications without requiring prior permission from the journal.
Third-Party Content
Authors are solely responsible for obtaining written permission to reproduce any third-party material, including figures, tables, images, data, or excerpts from other publications, included in their manuscript. Evidence of such permissions must be provided to the editorial office upon request. AOJSE does not assume any responsibility for copyright infringement arising from the unauthorized inclusion of third-party content in published articles.
Permitted Uses
Under the CC BY 4.0 license, the following uses of AOJSE published content are freely permitted without prior written permission from the journal or authors, provided that proper attribution is given. Users may read, download, print, and distribute articles for personal, educational, or research purposes. Researchers may reuse data, figures, and findings for meta-analyses, systematic reviews, and secondary research. Educators may incorporate published articles into course materials, syllabi, and academic presentations. Journalists, policymakers, and practitioners may reference and cite published findings for professional and public interest purposes.
Prohibited Uses
While the CC BY 4.0 license is broad and permissive, users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits. Users may not misrepresent the original authorship of published content or imply endorsement by the original authors or AOJSE without explicit consent. Plagiarism, data fabrication, and any form of academic misconduct in the reuse of published material are strictly prohibited and are a violation of publication ethics standards.
Institutional and Repository Deposit Policy
AOJSE fully supports self-archiving and green open access. Authors are encouraged to deposit the final published version of their article, also known as the version of record, in institutional repositories, subject repositories, and open-access databases immediately upon publication. There is no embargo period. Authors should always link back to the original article on the AOJSE website and include the full citation and DOI when depositing their work in any repository.
Digital Preservation and Archiving
AOJSE is committed to the long-term digital preservation of all published content to ensure its permanent availability and accessibility. The journal utilizes the Open Journal Systems (OJS) platform, which supports internationally recognized digital archiving standards. AOJSE encourages participation in archiving networks such as LOCKSS (Lots of Copies Keep Stuff Safe) and CLOCKSS (Controlled LOCKSS) to safeguard published articles against data loss and ensure continued access for future generations of researchers and readers.
Disclaimer
The views, opinions, findings, and conclusions expressed in articles published in AOJSE are solely those of the authors and do not necessarily reflect the official position, policy, or views of the journal, its editorial board, or its affiliated organizations. AOJSE makes every effort to ensure the accuracy and integrity of published content but does not accept legal responsibility for any errors, omissions, or claims arising from the use of information published in the journal.
Contact for Licensing Inquiries
For any questions regarding the licensing terms, copyright, or permitted use of content published in AOJSE, please contact the editorial office through the journal website at https://aojse.org. The editorial team will be happy to assist authors, readers, and institutions with any licensing-related queries.
