Machine Learning Approaches for Auto Insurance Big Data
Open accessAbstract
The growing trend in the number and severity of auto insurance claims creates a need for new methods to efficiently handle these claims. Machine learning (ML) is one of the methods that solves this problem. As car insurers aim to improve their customer service, these companies have started adopting and applying ML to enhance the interpretation and comprehension of their data for efficiency, thus improving their customer service through a better understanding of their needs. This study considers how automotive insurance providers incorporate machinery learning in their company, and explores how ML models can apply to insurance big data. We utilize various ML methods, such as logistic regression, XGBoost, random forest, decision trees, naïve Bayes, and K-NN, to predict claim occurrence. Furthermore, we evaluate and compare these models’ performances. The results showed that RF is better than other methods with the accuracy, kappa, and AUC values of 0.8677, 0.7117, and 0.840, respectively.
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Gradient boosting machines are flexible, can be customised to any data-driven task, and have proven considerable achievement in real-world applications.
“The approach is quite flexible and can be customised to any data-driven task and has proven considerable achievement in real-world applications (Hanafy and Ming 2021; Henckaerts et al. 2021).”
From Modelling Motor Insurance Claim Frequency and Severity Using Gradient Boosting (2023) · cites this paper as Hanafy and Ming 2021
The Related Work section reports successful uses of XGBoost for insurance-claim prediction, and the authors describe their own XGBoost result as useful. The Hyper-Parameter Optimization section shows that they tuned the model, but neither section establishes that gradient boosting machines can be customised to any data-driven task or have succeeded across real-world applications generally. Page numbers are unavailable in our copy. (LLM, conf=0.84)
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- Authors
- Hanafy, M., & Ming, R.
- Affiliations
- Zhejiang Gongshang University (CN)
- Corresponding author
- Hanafy, Mohamed
- Venue
- Risks
- Publisher
- MDPI AG
- Published
- 2021-02-20
- Volume
- 9
- Issue
- 2
- Pages
- 42
- ISSN
- 2227-9091 (ISSN-L)
- OpenAlex
- W3133357608
- Cited by
- 102
- References
- 43
- Citations by year
- 2026: 11 · 2025: 22 · 2024: 22 · 2023: 25 · 2022: 15 · 2021: 7
- FWCI
- 13.18
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Risks (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Econstor (Econstor) (submittedVersion, cc-by)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Insurance and Financial Risk Management
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Social Sciences › Business, Management and Accounting › Marketing › Customer churn and segmentation
- Keywords
- automobile insurance, insurance claims, big data, random forest, XGBoost, logistic regression, model comparison, customer service
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