To read this content please select one of the options below:
Please note you do not have access to teaching notes, automobile insurance fraud detection in the age of big data – a systematic and comprehensive literature review.
Journal of Financial Regulation and Compliance
ISSN : 1358-1988
Article publication date: 8 April 2022
Issue publication date: 2 August 2022
The purpose of this paper is to survey the automobile insurance fraud detection literature in the past 31 years (1990–2021) and present a research agenda that addresses the challenges and opportunities artificial intelligence and machine learning bring to car insurance fraud detection.
Design/methodology/approach
Content analysis methodology is used to analyze 46 peer-reviewed academic papers from 31 journals plus eight conference proceedings to identify their research themes and detect trends and changes in the automobile insurance fraud detection literature according to content characteristics.
This study found that automobile insurance fraud detection is going through a transformation, where traditional statistics-based detection methods are replaced by data mining- and artificial intelligence-based approaches. In this study, it was also noticed that cost-sensitive and hybrid approaches are the up-and-coming avenues for further research.
Practical implications
This paper’s findings not only highlight the rise and benefits of data mining- and artificial intelligence-based automobile insurance fraud detection but also highlight the deficiencies observable in this field such as the lack of cost-sensitive approaches or the absence of reliable data sets.
Originality/value
This paper offers greater insight into how artificial intelligence and data mining challenges traditional automobile insurance fraud detection models and addresses the need to develop new cost-sensitive fraud detection methods that identify new real-world data sets.
- Literature review
- Data mining
- Automobile insurance fraud detection
Benedek, B. , Ciumas, C. and Nagy, B.Z. (2022), "Automobile insurance fraud detection in the age of big data – a systematic and comprehensive literature review", Journal of Financial Regulation and Compliance , Vol. 30 No. 4, pp. 503-523. https://doi.org/10.1108/JFRC-11-2021-0102
Emerald Publishing Limited
Copyright © 2022, Emerald Publishing Limited
Related articles
All feedback is valuable.
Please share your general feedback
Report an issue or find answers to frequently asked questions
Contact Customer Support
Information
- Author Services
Initiatives
You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .
Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.
Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.
Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.
Original Submission Date Received: .
- Active Journals
- Find a Journal
- Proceedings Series
- For Authors
- For Reviewers
- For Editors
- For Librarians
- For Publishers
- For Societies
- For Conference Organizers
- Open Access Policy
- Institutional Open Access Program
- Special Issues Guidelines
- Editorial Process
- Research and Publication Ethics
- Article Processing Charges
- Testimonials
- Preprints.org
- SciProfiles
- Encyclopedia
Article Menu
- Subscribe SciFeed
- Recommended Articles
- Google Scholar
- on Google Scholar
- Table of Contents
Find support for a specific problem in the support section of our website.
Please let us know what you think of our products and services.
Visit our dedicated information section to learn more about MDPI.
JSmol Viewer
Exploring industry-level fairness of auto insurance premiums by statistical modeling of automobile rate and classification data.
1. Introduction
2. materials and methods, 2.1. background and data, 2.2. risk relativity estimates by generalized linear models, 2.3. distance measures for comparing premiums and loss costs, 2.4. modeling fixed effect using loss costs and premiums, 2.4.1. comparing loss costs and premiums within same territory, 2.4.2. comparing loss costs and premiums between territories, 4. conclusions, author contributions, data availability statement, conflicts of interest.
- Abraham, Kenneth S. 1985. Efficiency and fairness in insurance risk classification. Virginia Law Review , 403–51. [ Google Scholar ] [ CrossRef ]
- Arora, Nidhi, and Poonam Arora. 2014. Insurance Premium Optimization: Perspective of Insurance Seeker and Insurance Provider. Journal of Management and Science 4: 43–53. [ Google Scholar ] [ CrossRef ]
- Avraham, Ronen. 2017. Discrimination and Insurance. The Routledge Handbook to Discrimination Lippert-Rasmussen Ed, University of Texas Law, Law and Econ Research Paper No. E574. Available online: https://ssrn.com/abstract=3089946 or http://dx.doi.org/10.2139/ssrn.3089946 (accessed on 4 July 2022).
- Barry, Laurence. 2020. Insurance, big data and changing conceptions of fairness. European Journal of Sociology/Archives Européennes de Sociologie 61: 159–84. [ Google Scholar ] [ CrossRef ]
- Berry-Stölzle, Thomas R., and Patricia Born. 2012. The effect of regulation on insurance pricing: The case of Germany. Journal of Risk and Insurance 79: 129–64. [ Google Scholar ] [ CrossRef ]
- Cao, Longbing, Qiang Yang, and Philip S. Yu. 2021. Data science and AI in FinTech: An overview. International Journal of Data Science and Analytics 12: 81–99. [ Google Scholar ] [ CrossRef ]
- Charpentier, Arthur, Laurence Barry, and Molly R. James. 2022. Insurance against natural catastrophes: Balancing actuarial fairness and social solidarity. The Geneva Papers on Risk and Insurance-Issues and Practice 47: 50–78. [ Google Scholar ] [ CrossRef ]
- Chiappori, Pierre-André, Bruno Jullien, Bernard Salanié, and François Salanié. 2006. Asymmetric information in insurance: General testable implications. The RAND Journal of Economics 37: 783–98. [ Google Scholar ] [ CrossRef ]
- Cohen, Alma. 2005. Asymmetric information and learning: Evidence from the automobile insurance market. Review of Economics and Statistics 87: 197–207. [ Google Scholar ] [ CrossRef ]
- Cummins, J. David, and Mary A. Weiss. 1992. Regulation and the Automobile Insurance Crisis. Regulation 15: 48. [ Google Scholar ]
- Dugas, Charles, Yoshua Bengio, Nicolas Chapados, Pascal Vincent, Germain Denoncourt, and Christian Fournier. 2003. Statistical learning algorithms applied to automobile insurance ratemaking. In CAS Forum . Arlington: Casualty Actuarial Society, Vol. 1, pp. 179–214. [ Google Scholar ]
- Ferreira, Joseph, Jr., and Eric Minikel. 2012. Measuring per mile risk for pay-as-you-drive automobile insurance. Transportation Research Record 2297: 97–103. [ Google Scholar ] [ CrossRef ] [ Green Version ]
- Frees, Edward W., and Fei Huang. 2021. The discriminating (pricing) actuary. North American Actuarial Journal , 1–23. [ Google Scholar ] [ CrossRef ]
- Frezal, Sylvestre, and Laurence Barry. 2020. Fairness in uncertainty: Some limits and misinterpretations of actuarial fairness. Journal of Business Ethics 167: 127–36. [ Google Scholar ] [ CrossRef ]
- Grabowski, Henry, W. Kip Viscusi, and William N. Evans. 1989. Price and availability tradeoffs of automobile insurance regulation. Journal of Risk and Insurance 56: 275–99. [ Google Scholar ] [ CrossRef ]
- Hanafy, Mohamed, and Ruixing Ming. 2021. Machine learning approaches for auto insurance big data. Risks 9: 42. [ Google Scholar ] [ CrossRef ]
- Isotupa, K. P. Sapna, Mary Kelly, and Anna Kleffner. 2019. Experience-rating mechanisms in auto insurance: Implications for high-risk, low-risk, and novice drivers. North American Actuarial Journal 23: 395–411. [ Google Scholar ] [ CrossRef ]
- Landes, Xavier. 2015. How fair is actuarial fairness? Journal of Business Ethics 128: 519–33. [ Google Scholar ] [ CrossRef ] [ Green Version ]
- Liu, Lixin, Wenzhuo Li, Wu He, and Justin Zuopeng Zhang. 2022. Improve enterprise knowledge management with internet of things: A case study from auto insurance industry. Knowledge Management Research & Practice 20: 58–72. [ Google Scholar ]
- Medders, Lorilee A., Jamie A. Parson, and Matthew Thomas-Reid. 2021. Gender X and Auto Insurance: Is Gender Rating Unfairly Discriminatory? Journal of Insurance Regulation 40: 1–31. [ Google Scholar ] [ CrossRef ]
- Meyers, Gert, and Ine Van Hoyweghen. 2018. Enacting actuarial fairness in insurance: From fair discrimination to behaviour-based fairness. Science as Culture 27: 413–38. [ Google Scholar ] [ CrossRef ]
- Regan, Laureen, Sharon Tennyson, and Mary Weiss. 2008. The Relationship Between Auto Insurance Rate Regulation and Insured Loss Costs: An Empirical Analysis. Journal of Insurance Regulation 27. [ Google Scholar ]
- Ronka-Chmielowiec, Wanda, and Ewa Poprawska. 2005. Selected Methods of Credibility Theory and its Application to Calculating Insurance Premium in Heterogeneous Insurance Portfolios. In Innovations in Classification, Data Science, and Information Systems . Berlin and Heidelberg: Springer, pp. 490–97. [ Google Scholar ]
- Ryan, Stephen R. 1986. Elimination of Gender Discrimination in Insurance Pricing: Does Automobile Insurance Rate Without Sex. Notre Dame L. Rev. 61: 748. [ Google Scholar ]
- Saito, Kuniyoshi. 2006. Testing for asymmetric information in the automobile insurance market under rate regulation. Journal of Risk and Insurance 73: 335–56. [ Google Scholar ] [ CrossRef ]
- Schmeiser, Hato, Tina Störmer, and Joël Wagner. 2014. Unisex insurance pricing: Consumers’ perception and market implications. The Geneva Papers on Risk and Insurance-Issues and Practice 39: 322–50. [ Google Scholar ] [ CrossRef ] [ Green Version ]
- Sud, Keshav, Pakize Erdogmus, and Seifedine Kadry, eds. 2020. Introduction to Data Science and Machine Learning . Norderstedt: BoD–Books on Demand. [ Google Scholar ]
- Thiery, Yves, and Caroline Van Schoubroeck. 2006. Fairness and equality in insurance classification. The Geneva Papers on Risk and Insurance-Issues and Practice 31: 190–211. [ Google Scholar ] [ CrossRef ]
- Xin, Xi, and Fei Huang. 2022. Anti-Discrimination Insurance Pricing: Regulations, Fairness Criteria, and Models. Fairness Criteria, and Models . Available online: https://ssrn.com/abstract=3850420 (accessed on 4 July 2022). [ CrossRef ]
- Zahi, Jamal. 2021. Non-life insurance ratemaking techniques. International Journal of Accounting, Finance, Auditing, Management and Economics 2: 344–61. [ Google Scholar ]
Click here to enlarge figure
CLASS1 | $858 | $873 | $546 | $440 | $863 | $502 | $318 | $1381 | $1237 | $1074 | $985 | $949 | $580 | $440 |
CLASS2 | $1119 | $915 | $840 | $732 | $703 | $444 | $305 | $1824 | $1591 | $1428 | $1295 | $1115 | $810 | $526 |
CLASS3 | $813 | $615 | $568 | $1085 | $630 | $309 | $305 | $1714 | $1502 | $1342 | $1207 | $1097 | $787 | $521 |
CLASS5 | $56 | $68 | $61 | $162 | $104 | $56 | $88 | $193 | $162 | $147 | $130 | $114 | $93 | $80 |
CLASS6 | $62 | $49 | $76 | $59 | $517 | $75 | $208 | $389 | $332 | $317 | $313 | $258 | $199 | $199 |
CLASS7 | $270 | $389 | $237 | $650 | $537 | $305 | $243 | $1410 | $1571 | $1323 | $1510 | $1102 | $678 | $503 |
CLASS8 | $3966 | $ - | $413 | $634 | $ - | $ - | NA | $1600 | $1258 | $1364 | $1153 | $877 | $1001 | |
CLASS9 | $1678 | $2036 | $1495 | $2082 | $213 | $350 | $389 | $1673 | $1482 | $1445 | $1301 | $1117 | $887 | $658 |
CLASS10 | $397 | $1049 | $465 | $1138 | $311 | NA | NA | $1701 | $1363 | $1158 | $1184 | $1937 | ||
CLASS11 | $6096 | $467 | $590 | $317 | $140 | $150 | NA | $1767 | $1540 | $1425 | $1120 | $909 | $1298 | |
CLASS12 | $383 | $545 | $823 | $641 | $204 | $119 | $1412 | $1795 | $1475 | $1357 | $1175 | $1046 | $818 | $721 |
CLASS13 | $974 | $846 | $797 | $725 | $251 | $360 | $217 | $1826 | $1600 | $1343 | $1233 | $1078 | $897 | $680 |
CLASS18 | $886 | $558 | $465 | $522 | $367 | $168 | NA | $1429 | $1239 | $1118 | $989 | $865 | $879 | |
CLASS19 | $750 | $742 | $724 | $462 | $441 | $303 | $496 | $1549 | $1369 | $1207 | $1034 | $950 | $762 | $651 |
CLASS1 | $193 | $460 | $440 | $342 | $1129 | $229 | $169 | $865 | $756 | $649 | $582 | $533 | $422 | $259 |
CLASS2 | $466 | $582 | $896 | $811 | $868 | $211 | $222 | $1183 | $975 | $843 | $776 | $650 | $512 | $307 |
CLASS3 | $680 | $604 | $313 | $436 | $353 | $292 | $238 | $1213 | $990 | $878 | $835 | $713 | $551 | $352 |
CLASS5 | $54 | $59 | $238 | $29 | $2 | $0 | $69 | $142 | $116 | $100 | $80 | $67 | $56 | $47 |
CLASS6 | $27 | $9 | $33 | $314 | $ - | $6 | $54 | $347 | $291 | $253 | $229 | $187 | $149 | $137 |
CLASS7 | $14 | $25 | $188 | $11 | $537 | $146 | $111 | $995 | $1018 | $836 | $855 | $716 | $515 | $342 |
CLASS8 | $ - | $ - | $ - | $91 | $ - | $ - | NA | $1570 | $1153 | $1072 | $840 | $700 | $753 | |
CLASS9 | $621 | $ - | $366 | $192 | $253 | $44 | $371 | $1319 | $1042 | $935 | $829 | $731 | $572 | $436 |
CLASS10 | $852 | $99 | $119 | $196 | $ - | NA | NA | $1491 | $1120 | $1023 | $838 | $1535 | ||
CLASS11 | $510 | $102 | $2149 | $1036 | $61 | $50 | NA | $1797 | $1429 | $1241 | $833 | $691 | $866 | |
CLASS12 | $607 | $169 | $237 | $3255 | $139 | $1362 | $196 | $1702 | $1345 | $1165 | $898 | $825 | $595 | $509 |
CLASS13 | $404 | $22 | $554 | $11 | $273 | $77 | $145 | $1711 | $1245 | $1055 | $913 | $796 | $688 | $469 |
CLASS18 | $4215 | $300 | $137 | $336 | $136 | $109 | NA | $1206 | $1052 | $935 | $770 | $698 | $705 | |
CLASS19 | $586 | $684 | $365 | $1317 | $385 | $449 | $313 | $1167 | $966 | $833 | $707 | $660 | $561 | $454 |
2009 | 2010 | 2011 | ||||
---|---|---|---|---|---|---|
Gamma | Poisson | Gamma | Poisson | Gamma | Poisson | |
AB | ||||||
Rural | ||||||
DR | 0.30 | 0.12 | 0.06 | 0.06 | 0.12 | 0.17 |
CLASS | 0.18 | 0.16 | 0.16 | 0.16 | 0.17 | 0.16 |
Urban | ||||||
DR | 0.05 | 0.05 | 0.06 | 0.04 | 0.08 | 0.04 |
CLASS | 0.23 | 0.21 | 0.21 | 0.17 | 0.08 | 0.07 |
Rural | ||||||
DR | 0.26 | 0.22 | 0.12 | 0.11 | 0.12 | 0.11 |
CLASS | 0.11 | 0.11 | 0.10 | 0.11 | 0.16 | 0.16 |
Urban | ||||||
DR | 0.02 | 0.02 | 0.12 | 0.11 | 0.06 | 0.02 |
CLASS | 0.09 | 0.09 | 0.07 | 0.06 | 0.05 | 0.05 |
Rural | ||||||
DR | 0.05 | 0.06 | 0.04 | 0.03 | 0.08 | 0.02 |
CLASS | 0.07 | 0.07 | 0.20 | 0.18 | 0.09 | 0.06 |
Urban | ||||||
DR | 0.05 | 0.04 | 0.05 | 0.05 | 0.03 | 0.01 |
CLASS | 0.19 | 0.19 | 0.13 | 0.10 | 0.08 | 0.06 |
Gaussian | Poisson | Gamma | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Term | Estimate | Standard | Statistic | p-Value | Loss | Estimate | Standard | Statistic | p-Value | Loss | Estimate | Standard | Statistic | p-Value | Loss |
Error | Relativity | Error | Relativity | Error | Relativity | ||||||||||
(Intercept) | 6.79 | 0.05 | 147.23 | 0.00 | 6.79 | 0.00 | 57,577.37 | 0.00 | 6.75 | 0.11 | 61.10 | 0.00 | |||
DR0 | 0.00 | 1.54 | 0.00 | 1.53 | 0.00 | 1.57 | |||||||||
DR1 | −0.21 | 0.05 | −4.33 | 0.00 | 1.25 | −0.21 | 0.00 | −1412.51 | 0.00 | 1.25 | −0.22 | 0.14 | −1.62 | 0.11 | 1.26 |
DR2 | −0.36 | 0.05 | −7.53 | 0.00 | 1.07 | −0.35 | 0.00 | −2490.09 | 0.00 | 1.09 | −0.27 | 0.13 | −2.13 | 0.03 | 1.20 |
DR3 | −0.43 | 0.04 | −9.82 | 0.00 | 1.00 | −0.43 | 0.00 | −3359.13 | 0.00 | 1.00 | −0.45 | 0.12 | −3.86 | 0.00 | 1.00 |
DR4 | −0.60 | 0.05 | −11.73 | 0.00 | 0.85 | −0.58 | 0.00 | −4188.70 | 0.00 | 0.86 | −0.55 | 0.13 | −4.41 | 0.00 | 0.91 |
DR5 | −0.93 | 0.04 | −20.80 | 0.00 | 0.61 | −0.91 | 0.00 | −7455.19 | 0.00 | 0.62 | −0.87 | 0.11 | −7.61 | 0.00 | 0.66 |
DR6 | −1.37 | 0.04 | −36.68 | 0.00 | 0.39 | −1.35 | 0.00 | −12,133.80 | 0.00 | 0.40 | −1.30 | 0.11 | −11.91 | 0.00 | 0.43 |
CLASS1 | 0.00 | 0.85 | 0.00 | 0.86 | 0.00 | 0.86 | |||||||||
CLASS2 | 0.16 | 0.02 | 7.48 | 0.00 | 1.00 | 0.16 | 0.00 | 4200.70 | 0.00 | 1.00 | 0.15 | 0.02 | 7.47 | 0.00 | 1.00 |
CLASS3 | 0.00 | 0.04 | 0.07 | 0.94 | 0.85 | 0.05 | 0.00 | 810.88 | 0.00 | 0.90 | 0.09 | 0.04 | 2.56 | 0.01 | 0.94 |
CLASS5 | −2.17 | 0.35 | −6.23 | 0.00 | 0.10 | −2.11 | 0.00 | −7428.64 | 0.00 | 0.10 | −2.01 | 0.09 | −23.33 | 0.00 | 0.12 |
CLASS6 | −1.49 | 0.16 | −9.37 | 0.00 | 0.19 | −1.42 | 0.00 | −7674.17 | 0.00 | 0.21 | −1.29 | 0.08 | −16.34 | 0.00 | 0.24 |
CLASS7 | −0.12 | 0.07 | −1.63 | 0.10 | 0.76 | −0.07 | 0.00 | −629.56 | 0.00 | 0.80 | −0.05 | 0.06 | −0.86 | 0.39 | 0.81 |
CLASS8 | 0.79 | 0.21 | 3.85 | 0.00 | 1.88 | 0.68 | 0.00 | 871.21 | 0.00 | 1.70 | 0.59 | 0.87 | 0.67 | 0.50 | 1.54 |
CLASS9 | 0.47 | 0.11 | 4.45 | 0.00 | 1.36 | 0.43 | 0.00 | 1497.78 | 0.00 | 1.32 | 0.40 | 0.23 | 1.80 | 0.07 | 1.29 |
CLASS10 | 0.16 | 0.09 | 1.75 | 0.08 | 1.00 | 0.18 | 0.00 | 746.47 | 0.00 | 1.02 | 0.25 | 0.21 | 1.24 | 0.22 | 1.11 |
CLASS11 | 0.14 | 0.07 | 2.01 | 0.05 | 0.98 | 0.16 | 0.00 | 900.32 | 0.00 | 1.00 | 0.22 | 0.15 | 1.51 | 0.13 | 1.07 |
CLASS12 | 0.20 | 0.06 | 3.26 | 0.00 | 1.04 | 0.27 | 0.00 | 1891.16 | 0.00 | 1.12 | 0.41 | 0.11 | 3.61 | 0.00 | 1.29 |
CLASS13 | 0.21 | 0.06 | 3.32 | 0.00 | 1.05 | 0.25 | 0.00 | 1719.57 | 0.00 | 1.10 | 0.28 | 0.10 | 2.74 | 0.01 | 1.14 |
CLASS18 | −0.11 | 0.07 | −1.53 | 0.13 | 0.76 | −0.06 | 0.00 | −385.79 | 0.00 | 0.81 | 0.03 | 0.12 | 0.24 | 0.81 | 0.88 |
CLASS19 | 0.04 | 0.05 | 0.69 | 0.49 | 0.88 | 0.10 | 0.00 | 933.66 | 0.00 | 0.95 | 0.20 | 0.07 | 2.67 | 0.01 | 1.05 |
Fixed Effect | 0.03 | 0.02 | 1.85 | 0.07 | −0.03 | 0.00 | −789.06 | 0.00 | −0.08 | 0.02 | −4.48 | 0.00 | |||
Territory | 0.61 | 0.03 | 23.33 | 0.00 | 0.62 | 0.00 | 15,838.14 | 0.00 | 0.64 | 0.02 | 33.16 | 0.00 |
Gaussian | Poisson | Gamma | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Term | Estimate | Standard | Statistic | p-Value | Loss | Estimate | Standard | Statistic | p-Value | Loss | Estimate | Standard | Statistic | p-Value | Loss |
Error | Relativity | Error | Relativity | Error | Relativity | ||||||||||
(Intercept) | 5.97 | 0.04 | 152.22 | 0.00 | 5.94 | 0.00 | 53,997.66 | 0.00 | 5.92 | 0.08 | 78.42 | 0.00 | |||
DR0 | 0.00 | 1.49 | 0.00 | 1.49 | 0.00 | 1.51 | |||||||||
DR1 | −0.15 | 0.04 | −3.29 | 0.00 | 1.29 | −0.14 | 0.00 | −1049.44 | 0.00 | 1.29 | −0.15 | 0.09 | −1.62 | 0.11 | 1.29 |
DR2 | −0.21 | 0.04 | −4.87 | 0.00 | 1.22 | −0.21 | 0.00 | −1641.89 | 0.00 | 1.20 | −0.23 | 0.09 | −2.61 | 0.01 | 1.19 |
DR3 | −0.40 | 0.04 | −10.06 | 0.00 | 1.00 | −0.40 | 0.00 | −3338.02 | 0.00 | 1.00 | −0.41 | 0.08 | −5.08 | 0.00 | 1.00 |
DR4 | −0.55 | 0.05 | −11.52 | 0.00 | 0.87 | −0.55 | 0.00 | −4140.26 | 0.00 | 0.86 | −0.55 | 0.09 | −6.41 | 0.00 | 0.87 |
DR5 | −0.76 | 0.04 | −18.39 | 0.00 | 0.70 | −0.76 | 0.00 | −6575.99 | 0.00 | 0.70 | −0.77 | 0.08 | −9.80 | 0.00 | 0.70 |
DR6 | −1.06 | 0.03 | −30.24 | 0.00 | 0.52 | −1.05 | 0.00 | −10,009.82 | 0.00 | 0.52 | −1.05 | 0.07 | −14.18 | 0.00 | 0.53 |
CLASS1 | 0.00 | 0.89 | 0.00 | 0.92 | 0.00 | 0.93 | |||||||||
CLASS2 | 0.11 | 0.02 | 6.19 | 0.00 | 1.00 | 0.09 | 0.00 | 2582.05 | 0.00 | 1.00 | 0.07 | 0.01 | 4.69 | 0.00 | 1.00 |
CLASS3 | 0.14 | 0.03 | 4.88 | 0.00 | 1.03 | 0.15 | 0.00 | 2710.72 | 0.00 | 1.06 | 0.16 | 0.02 | 6.45 | 0.00 | 1.10 |
CLASS5 | −1.42 | 0.17 | −8.18 | 0.00 | 0.22 | −1.39 | 0.00 | −6840.65 | 0.00 | 0.23 | −1.36 | 0.06 | −22.13 | 0.00 | 0.24 |
CLASS6 | −0.72 | 0.08 | −9.08 | 0.00 | 0.43 | −0.71 | 0.00 | −5235.60 | 0.00 | 0.45 | −0.65 | 0.06 | −11.63 | 0.00 | 0.49 |
CLASS7 | 0.26 | 0.04 | 5.87 | 0.00 | 1.15 | 0.25 | 0.00 | 2805.29 | 0.00 | 1.18 | 0.24 | 0.04 | 5.56 | 0.00 | 1.19 |
CLASS8 | 0.42 | 0.29 | 1.43 | 0.15 | 1.35 | 0.39 | 0.00 | 439.09 | 0.00 | 1.35 | 0.36 | 0.61 | 0.59 | 0.55 | 1.34 |
CLASS9 | 0.50 | 0.10 | 5.10 | 0.00 | 1.46 | 0.48 | 0.00 | 1823.17 | 0.00 | 1.48 | 0.46 | 0.16 | 2.89 | 0.00 | 1.48 |
CLASS10 | 0.84 | 0.05 | 16.12 | 0.00 | 2.06 | 0.85 | 0.00 | 4870.39 | 0.00 | 2.14 | 0.87 | 0.14 | 6.02 | 0.00 | 2.23 |
CLASS11 | 0.63 | 0.05 | 14.03 | 0.00 | 1.68 | 0.64 | 0.00 | 4665.35 | 0.00 | 1.74 | 0.67 | 0.10 | 6.55 | 0.00 | 1.83 |
CLASS12 | 0.61 | 0.04 | 14.54 | 0.00 | 1.64 | 0.65 | 0.00 | 5553.23 | 0.00 | 1.75 | 0.72 | 0.08 | 8.95 | 0.00 | 1.92 |
CLASS13 | 0.50 | 0.05 | 10.79 | 0.00 | 1.48 | 0.50 | 0.00 | 4169.58 | 0.00 | 1.52 | 0.51 | 0.07 | 7.06 | 0.00 | 1.56 |
CLASS18 | 0.25 | 0.05 | 4.99 | 0.00 | 1.15 | 0.27 | 0.00 | 1989.33 | 0.00 | 1.20 | 0.30 | 0.08 | 3.53 | 0.00 | 1.26 |
CLASS19 | 0.22 | 0.04 | 5.13 | 0.00 | 1.11 | 0.23 | 0.00 | 2371.56 | 0.00 | 1.15 | 0.26 | 0.05 | 4.92 | 0.00 | 1.21 |
Fixed Effect | −0.22 | 0.02 | −14.49 | 0.00 | −0.21 | 0.00 | −7273.30 | 0.00 | −0.21 | 0.01 | −16.43 | 0.00 | |||
Territory | 0.29 | 0.02 | 16.63 | 0.00 | 0.34 | 0.00 | 10,279.17 | 0.00 | 0.37 | 0.01 | 27.07 | 0.00 |
Gaussian | Poisson | Gamma | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Term | Estimate | Standard | Statistic | p-Value | Loss | Estimate | Standard | Statistic | p-Value | Loss | Estimate | Standard | Statistic | p-Value | Loss |
Error | Relativity | Error | Relativity | Error | Relativity | ||||||||||
(Intercept) | 6.12 | 0.03 | 224.61 | 0.00 | 6.11 | 0.00 | 21,740.80 | 0.00 | 6.10 | 0.07 | 88.68 | 0.00 | |||
DR0 | 0.00 | 1.32 | 0.00 | 1.31 | 0.00 | 1.34 | |||||||||
DR1 | −0.08 | 0.03 | −2.46 | 0.01 | 1.22 | −0.07 | 0.00 | −192.63 | 0.00 | 1.23 | −0.05 | 0.09 | −0.57 | 0.57 | 1.27 |
DR2 | −0.23 | 0.03 | −7.10 | 0.00 | 1.05 | −0.24 | 0.00 | −706.65 | 0.00 | 1.03 | −0.26 | 0.08 | −3.19 | 0.00 | 1.03 |
DR3 | −0.28 | 0.03 | −9.74 | 0.00 | 1.00 | −0.27 | 0.00 | −890.90 | 0.00 | 1.00 | −0.29 | 0.07 | −3.96 | 0.00 | 1.00 |
DR4 | −0.41 | 0.03 | −12.55 | 0.00 | 0.88 | −0.41 | 0.00 | −1242.49 | 0.00 | 0.87 | −0.44 | 0.08 | −5.57 | 0.00 | 0.86 |
DR5 | −0.53 | 0.03 | −19.13 | 0.00 | 0.78 | −0.53 | 0.00 | −1840.77 | 0.00 | 0.77 | −0.53 | 0.07 | −7.48 | 0.00 | 0.79 |
DR6 | −0.90 | 0.03 | −34.84 | 0.00 | 0.54 | −0.91 | 0.00 | −3301.03 | 0.00 | 0.53 | −0.90 | 0.07 | −13.29 | 0.00 | 0.54 |
CLASS1 | 0.00 | 0.90 | 0.00 | 0.91 | 0.00 | 0.92 | |||||||||
CLASS2 | 0.11 | 0.01 | 10.06 | 0.00 | 1.00 | 0.09 | 0.00 | 1348.34 | 0.00 | 1.00 | 0.08 | 0.01 | 7.25 | 0.00 | 1.00 |
CLASS3 | 0.17 | 0.02 | 10.77 | 0.00 | 1.06 | 0.18 | 0.00 | 1620.74 | 0.00 | 1.09 | 0.19 | 0.02 | 9.96 | 0.00 | 1.11 |
CLASS5 | −1.33 | 0.08 | −15.89 | 0.00 | 0.24 | −1.27 | 0.00 | −3424.25 | 0.00 | 0.26 | −1.16 | 0.05 | −25.49 | 0.00 | 0.29 |
CLASS6 | −0.63 | 0.04 | −16.00 | 0.00 | 0.48 | −0.64 | 0.00 | −2476.69 | 0.00 | 0.48 | −0.61 | 0.04 | −14.44 | 0.00 | 0.50 |
CLASS7 | 0.28 | 0.02 | 11.95 | 0.00 | 1.20 | 0.27 | 0.00 | 1505.80 | 0.00 | 1.19 | 0.26 | 0.03 | 7.85 | 0.00 | 1.19 |
CLASS8 | 0.82 | 0.17 | 4.92 | 0.00 | 2.04 | 0.84 | 0.00 | 390.00 | 0.00 | 2.10 | 0.88 | 0.69 | 1.27 | 0.21 | 2.21 |
CLASS9 | 0.59 | 0.06 | 9.79 | 0.00 | 1.63 | 0.61 | 0.00 | 1011.07 | 0.00 | 1.67 | 0.63 | 0.14 | 4.35 | 0.00 | 1.72 |
CLASS10 | 1.00 | 0.04 | 23.46 | 0.00 | 2.46 | 1.00 | 0.00 | 1706.97 | 0.00 | 2.47 | 1.02 | 0.20 | 5.02 | 0.00 | 2.56 |
CLASS11 | 0.84 | 0.03 | 27.23 | 0.00 | 2.09 | 0.83 | 0.00 | 2175.64 | 0.00 | 2.08 | 0.84 | 0.12 | 7.09 | 0.00 | 2.13 |
CLASS12 | 0.74 | 0.03 | 27.23 | 0.00 | 1.88 | 0.74 | 0.00 | 2520.96 | 0.00 | 1.91 | 0.77 | 0.08 | 9.59 | 0.00 | 1.98 |
CLASS13 | 0.73 | 0.03 | 27.27 | 0.00 | 1.86 | 0.74 | 0.00 | 2750.60 | 0.00 | 1.91 | 0.75 | 0.07 | 11.28 | 0.00 | 1.96 |
CLASS18 | 0.48 | 0.03 | 15.61 | 0.00 | 1.46 | 0.49 | 0.00 | 1520.96 | 0.00 | 1.48 | 0.52 | 0.08 | 6.25 | 0.00 | 1.55 |
CLASS19 | 0.44 | 0.02 | 19.86 | 0.00 | 1.40 | 0.47 | 0.00 | 2365.80 | 0.00 | 1.45 | 0.50 | 0.04 | 11.49 | 0.00 | 1.52 |
Fixed Effect | −0.50 | 0.01 | −52.27 | 0.00 | −0.50 | 0.00 | −7957.49 | 0.00 | −0.50 | 0.01 | −48.62 | 0.00 | |||
Territory | 0.11 | 0.01 | 12.03 | 0.00 | 0.13 | 0.00 | 2033.71 | 0.00 | 0.15 | 0.01 | 14.22 | 0.00 |
2009 | 2010 | 2011 | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Gaussian | Poisson | Gamma | Inverse | Gaussian | Poisson | Gamma | Inverse | Gaussian | Poisson | Gamma | Inverse | |
Gaussian | Gaussian | Gaussian | ||||||||||
AB | ||||||||||||
Rural | ||||||||||||
DR | 1.24 | 1.22 | 1.21 | 1.20 | 1.44 | 1.45 | 1.45 | 1.45 | 1.41 | 1.42 | 1.44 | 1.45 |
CLASS | 1.32 | 1.24 | 1.20 | 1.22 | 1.51 | 1.46 | 1.46 | 1.64 | 1.40 | 1.45 | 1.49 | 1.52 |
Urban | ||||||||||||
DR | 0.70 | 0.71 | 0.71 | 0.73 | 0.82 | 0.83 | 0.84 | 0.86 | 1.65 | 1.63 | 1.62 | 1.62 |
CLASS | 0.69 | 0.70 | 0.70 | 0.71 | 0.80 | 0.82 | 0.83 | 0.83 | 1.62 | 1.60 | 1.59 | 1.58 |
TPL | ||||||||||||
Rural | ||||||||||||
DR | 1.40 | 1.44 | 1.46 | 1.48 | 1.32 | 1.32 | 1.32 | 1.33 | 1.28 | 1.30 | 1.32 | 1.33 |
CLASS | 1.45 | 1.46 | 1.44 | 1.42 | 1.35 | 1.32 | 1.31 | 1.29 | 1.26 | 1.33 | 1.35 | 1.36 |
Urban | ||||||||||||
DR | 1.40 | 1.44 | 1.46 | 1.48 | 1.15 | 1.16 | 1.17 | 1.17 | 1.33 | 1.29 | 1.27 | 1.26 |
CLASS | 1.14 | 1.13 | 1.12 | 1.10 | 1.15 | 1.14 | 1.13 | 1.12 | 1.32 | 1.29 | 1.27 | 1.25 |
COL | ||||||||||||
Rural | ||||||||||||
DR | 1.82 | 1.83 | 1.84 | 1.85 | 1.82 | 1.84 | 1.86 | 1.86 | 1.68 | 1.69 | 1.70 | 1.70 |
CLASS | 1.84 | 1.82 | 1.80 | 1.78 | 1.84 | 1.83 | 1.82 | 1.81 | 1.66 | 1.67 | 1.67 | 1.66 |
Urban | ||||||||||||
DR | 1.65 | 1.66 | 1.66 | 1.67 | 1.61 | 1.62 | 1.62 | 1.63 | 1.56 | 1.55 | 1.55 | 1.54 |
CLASS | 1.64 | 1.64 | 1.63 | 1.61 | 1.61 | 1.60 | 1.59 | 1.58 | 1.54 | 1.53 | 1.53 | 1.51 |
2009 | 2010 | 2011 | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Gaussian | Poisson | Gamma | Inverse | Gaussian | Poisson | Gamma | Inverse | Gaussian | Poisson | Gamma | Inverse | |
Gaussian | Gaussian | Gaussian | ||||||||||
AB | ||||||||||||
Rural | ||||||||||||
Fixed Effect | 1.30 | 1.23 | 1.21 | NA | 1.43 | 1.45 | 1.48 | NA | 1.36 | 1.44 | 1.49 | NA |
Urban | ||||||||||||
Fixed Effect | 0.67 | 0.70 | 0.72 | 0.74 | 0.78 | 0.82 | 0.84 | 0.84 | 1.65 | 1.62 | 1.60 | 1.59 |
TPL | ||||||||||||
Rural | ||||||||||||
Fixed Effect | 1.40 | 1.45 | 1.47 | 1.46 | 1.34 | 1.32 | 1.31 | 1.31 | 1.23 | 1.32 | 1.36 | 1.38 |
Urban | ||||||||||||
Fixed Effect | 1.14 | 1.14 | 1.12 | 1.10 | 1.14 | 1.15 | 1.15 | 1.13 | 1.35 | 1.29 | 1.26 | 1.24 |
Rural | ||||||||||||
Fixed Effect | 1.83 | 1.82 | 1.81 | 1.80 | 1.81 | 1.82 | 1.83 | 1.82 | 1.64 | 1.67 | 1.67 | NA |
Urban | ||||||||||||
Fixed Effect | 1.63 | 1.64 | 1.64 | 1.62 | 1.59 | 1.60 | 1.60 | 1.58 | 1.54 | 1.54 | 1.52 | 1.50 |
Average Effect | 1.23 | 1.24 | 1.24 | NA | 1.23 | 1.24 | 1.24 | NA | 1.40 | 1.40 | 1.39 | NA |
2009 | 2010 | 2011 | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Gaussian | Poisson | Gamma | Inverse | Gaussian | Poisson | Gamma | Inverse | Gaussian | Poisson | Gamma | Inverse | |
Gaussian | Gaussian | Gaussian | ||||||||||
AB | ||||||||||||
Rural | ||||||||||||
Fixed Effect | 0.72 | 0.79 | 0.86 | NA | 0.84 | 0.93 | 1.02 | NA | 1.61 | 1.57 | 1.55 | NA |
Urban | ||||||||||||
Fixed Effect | 1.54 | 1.60 | 1.71 | NA | 1.71 | 1.85 | 2.08 | NA | 2.44 | 2.45 | 2.47 | NA |
TPL | ||||||||||||
Rural | ||||||||||||
Fixed Effect | 1.20 | 1.21 | 1.23 | 0.98 | 1.18 | 1.20 | 1.20 | 1.20 | 1.33 | 1.30 | 1.29 | 1.28 |
Urban | ||||||||||||
Fixed Effect | 1.70 | 1.79 | 1.89 | 1.00 | 1.62 | 1.68 | 1.71 | 1.74 | 1.66 | 1.74 | 1.80 | 1.84 |
COL | ||||||||||||
Rural | ||||||||||||
Fixed Effect | 1.69 | 1.70 | 1.70 | 1.69 | 1.66 | 1.67 | 1.68 | 1.68 | 1.57 | 1.58 | 1.57 | 1.56 |
Urban | ||||||||||||
Fixed Effect | 1.89 | 1.94 | 1.97 | 1.98 | 1.86 | 1.93 | 1.98 | 2.02 | 1.75 | 1.79 | 1.82 | 1.83 |
Average Effect | 1.50 | 1.56 | 1..63 | NA | 1.50 | 1.55 | 1.61 | NA | 1.69 | 1.72 | 1.75 | NA |
Two-Way Combined | Three-Way Combined | |||||
---|---|---|---|---|---|---|
Gaussian | Poisson | Gamma | Gaussian | Poisson | Gamma | |
AB | ||||||
Rural | ||||||
Fixed Effect (CLASS&DR) | 1.38 | 1.37 | 1.37 | 0.97 | 1.03 | 1.09 |
Urban | ||||||
Fixed Effect (CLASS&DR) | 0.91 | 0.94 | 0.95 | 1.78 | 1.90 | 2.06 |
Rural | ||||||
Fixed Effect (CLASS&DR) | 1.34 | 1.36 | 1.37 | 1.24 | 1.24 | 1.24 |
Urban | ||||||
Fixed Effect (CLASS&DR) | 1.21 | 1.19 | 1.17 | 1.66 | 1.73 | 1.79 |
Rural | ||||||
Fixed Effect (CLASS&DR) | 1.76 | 1.77 | 1.77 | 1.64 | 1.65 | 1.65 |
Urban | ||||||
Fixed Effect (CLASS&DR) | 1.59 | 1.59 | 1.58 | 1.83 | 1.88 | 1.92 |
Average Fixed Effect | 1.28 | 1.28 | 1.27 | 1.54 | 1.60 | 1.65 |
MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
Share and Cite
Xie, S.; Luo, R.; Li, Y. Exploring Industry-Level Fairness of Auto Insurance Premiums by Statistical Modeling of Automobile Rate and Classification Data. Risks 2022 , 10 , 194. https://doi.org/10.3390/risks10100194
Xie S, Luo R, Li Y. Exploring Industry-Level Fairness of Auto Insurance Premiums by Statistical Modeling of Automobile Rate and Classification Data. Risks . 2022; 10(10):194. https://doi.org/10.3390/risks10100194
Xie, Shengkun, Rebecca Luo, and Yuanshun Li. 2022. "Exploring Industry-Level Fairness of Auto Insurance Premiums by Statistical Modeling of Automobile Rate and Classification Data" Risks 10, no. 10: 194. https://doi.org/10.3390/risks10100194
Article Metrics
Article access statistics, further information, mdpi initiatives, follow mdpi.
Subscribe to receive issue release notifications and newsletters from MDPI journals
Advertisement
A data science approach to risk assessment for automobile insurance policies
- Regular Paper
- Published: 22 March 2023
- Volume 17 , pages 127–138, ( 2024 )
Cite this article
- Patrick Hosein 1
531 Accesses
7 Citations
1 Altmetric
Explore all metrics
In order to determine a suitable automobile insurance policy premium, one needs to take into account three factors: the risk associated with the drivers and cars on the policy, the operational costs associated with management of the policy and the desired profit margin. The premium should then be some function of these three values. We focus on risk assessment using a data science approach. Instead of using the traditional frequency and severity metrics, we instead predict the total claims that will be made by a new customer using historical data of current and past policies. Given multiple features of the policy (age and gender of drivers, value of car, previous accidents, etc.), one can potentially try to provide personalized insurance policies based specifically on these features as follows. We can compute the average claims made per year of all past and current policies with identical features and then take an average over these claim rates. Unfortunately there may not be sufficient samples to obtain a robust average. We can instead try to include policies that are “similar” to obtain sufficient samples for a robust average. We therefore face a trade-off between personalization (only using closely similar policies) and robustness (extending the domain far enough to capture sufficient samples). This is known as the bias–variance trade-off. We model this problem and determine the optimal trade-off between the two (i.e., the balance that provides the highest prediction accuracy) and apply it to the claim rate prediction problem. We demonstrate our approach using real data.
This is a preview of subscription content, log in via an institution to check access.
Access this article
Subscribe and save.
- Get 10 units per month
- Download Article/Chapter or eBook
- 1 Unit = 1 Article or 1 Chapter
- Cancel anytime
Price includes VAT (Russian Federation)
Instant access to the full article PDF.
Rent this article via DeepDyve
Institutional subscriptions
Similar content being viewed by others
Risk Assessment for Personalized Health Insurance Products
A “pay-how-you-drive” car insurance approach through cluster analysis
Improving automobile insurance ratemaking using telematics: incorporating mileage and driver behaviour data
Availability of data and materials.
The data used for this publication are confidential, and hence, we are only permitted to provide results but cannot share the data.
Code Availability
The code used to generate results is also proprietary to the company, but we hope that our pseudo-code can be used if one wishes to apply the model to their datasets.
Albrecher, H., Bommier, A., Filipović, D., et al.: Insurance: models, digitalization, and data science. Eur. Actuar. J. 9 , 349–360 (2019)
Article MathSciNet Google Scholar
Bian, Y., Yang, C., Zhao, J.L., et al.: Good drivers pay less: a study of usage-based vehicle insurance models. Transp. Res. A: Policy Pract. 107 , 20–34 (2018). https://doi.org/10.1016/j.tra.2017.10.018
Article Google Scholar
David, M., Jemna, D.V.: Modeling the frequency of auto insurance claims by means of poisson and negative binomial models. Analele stiintifice ale Universitatii “Al I Cuza” din Iasi Stiinte economice/Scientific Annals of the“ Al I Cuza” (2015)
Denuit, M., Trufin, J.: Effective Statistical Learning Methods for Actuaries. Springer Actuarial Lecture Notes (2019)
Errais, E.: Pricing insurance premia: a top down approach. Annals of Operations Research, pp. 1–16 (2019)
Esfandabadi, Z.S., Ranjbari, M., Scagnelli, S.D.: (0) Prioritizing risk-level factors in comprehensive automobile insurance management: A hybrid multi-criteria decision-making model. Glob. Bus. Rev. https://doi.org/10.1177/0972150920932287 ,
Guelman, L.: Gradient boosting trees for auto insurance loss cost modeling and prediction. Expert Syst. Appl. 39 (3), 3659–3667 (2012)
Hanafy, M., Ming, R.: Machine learning approaches for auto insurance big data. Risks 9 (2), 42 (2021)
Hassani, H., Unger, S., Beneki, C.: Big data and actuarial science. Big Data Cogn. Comput. 4 , 40 (2020)
He, B., Zhang, D., Liu, S., et al.: Profiling driver behavior for personalized insurance pricing and maximal profit. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 1387–1396. https://doi.org/10.1109/BigData.2018.8622491 (2018)
Hosein, P.: On the prediction of automobile insurance claims: the personalization versus confidence trade-off. In: 2021 IEEE International Conference on Technology Management, pp. 1–6. Operations and Decisions (ICTMOD), IEEE (2021)
Hosein, P., Rahaman, I., Nichols, K., et al.: Recommendations for long-term profit optimization. In: ImpactRS@ RecSys (2019)
Jeong, H., Valdez, E.A.: Predictive compound risk models with dependence. Insurance Math. Econom. 94 , 182–195 (2020)
Kanchinadam, T., Qazi, M., Bockhorst, J., et al.: Using discriminative graphical models for insurance recommender systems. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 421–428 (2018). https://doi.org/10.1109/ICMLA.2018.00069
Liu, Y., Wang, B.J., Lv, S.G.: Using multi-class adaboost tree for prediction frequency of auto insurance. J. Appl. Finance Bank. 4 (5), 45 (2014)
Google Scholar
Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., et al. (Eds.) Advances in Neural Information Processing Systems, vol 30. Curran Associates, Inc (2017). https://proceedings.neurips.cc/paper/2017/file/8a20a8621978632d76c43dfd28b67767-Paper.pdf
Qazi, M., Fung, G.M., Meissner, K.J., et al.: An insurance recommendation system using bayesian networks. In: Proceedings of the Eleventh ACM Conference on Recommender Systems. Association for Computing Machinery, New York, NY, USA, RecSys ’17, pp. 274–278 (2017). https://doi.org/10.1145/3109859.3109907
Qazi, M., Tollas, K., Kanchinadam, T., et al.: Designing and deploying insurance recommender systems using machine learning. WIREs Data Min. Knowl. Discovery 10 (4), e1363 (2020). https://doi.org/10.1002/widm.1363
Su, X., Bai, M.: Stochastic gradient boosting frequency-severity model of insurance claims. PLoS ONE 15 (8), e0238000 (2020)
Zhang, Y., Dukic, V.: Predicting multivariate insurance loss payments under the bayesian copula framework. J. Risk Insurance 80 (4), 891–919 (2013)
Download references
The authors did not receive support from any organization for the submitted work.
Author information
Authors and affiliations.
Department of Computer Science, The University of the West Indies, St. Augustine, Trinidad and Tobago
Patrick Hosein
You can also search for this author in PubMed Google Scholar
Contributions
The sole author performed the research, wrote the code for evaluating the solution and wrote the entire paper
Corresponding author
Correspondence to Patrick Hosein .
Ethics declarations
Conflict of interest.
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Ethics approval
Not applicable.
Consent to participate
Consent for publication, additional information, publisher's note.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Reprints and permissions
About this article
Hosein, P. A data science approach to risk assessment for automobile insurance policies. Int J Data Sci Anal 17 , 127–138 (2024). https://doi.org/10.1007/s41060-023-00392-x
Download citation
Received : 13 September 2022
Accepted : 05 March 2023
Published : 22 March 2023
Issue Date : January 2024
DOI : https://doi.org/10.1007/s41060-023-00392-x
Share this article
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative
- Motor insurance
- Machine learning
- Premium pricing
- Claims prediction
Mathematics subject classification
- Find a journal
- Publish with us
- Track your research
- Insights and Resources
- White Paper
- Auto Insurance Trends Report
LexisNexis® U.S. Auto Insurance Trends Report
Top 5 auto insurance trends to watch.
The annual LexisNexis® Risk Solutions U.S. Auto Insurance Trends Report explores key trends from the previous year and offers insights to help insurers make more informed business decisions. This year, we identified five trends impacting U.S. consumer auto insurance shopping, claims, driving violations and more.
As U.S. auto insurers steer through the challenges of the current market, these trends offer insights to help navigate the road ahead. Insurers can leverage proprietary and industry consortium data to help gain a clearer view of performance benchmarks and plan for the road ahead.
Access the report to explore :
- High claim severities show little signs of slowing down
- Insurers take aggressive steps to address profitability challenges
- Consumers respond to market conditions by shopping and switching auto policies
- Risky driving behavior persists
- As electric vehicle sales grow, so do insurance risks
Access the report now
We appreciate your interest.
Explore each section to gain additional insights
High claim severities show little signs of slowing down.
- Sustained rise in claims severity
- Increase in uninsured motorist and attorney-represented claims
- Total loss claims
- Length of repair times
- Rising costs of medical bills, towing services and storage costs
Trend Details
Both the severity and frequency of claims, including severe auto physical damage and bodily injury, have increased since 2020. Bodily injury severity has increased 20% in the post-pandemic years.
More than a quarter of collision claims were deemed total losses in 2023. While that percentage is the same as the previous year, total loss claims have jumped 29% since 2020.
More claimants are seeking advice from attorneys before settling. In fact, according to our 2023 survey, a majority of claimants who hired an attorney for their last claim would most likely do so again.
Consumers respond sharply to market conditions by shopping and switching auto policies
As rate increases went into effect throughout 2023, many consumers reacted by shopping for lower policy prices. Many of those who shopped for lower rates ended up switching insurers, resulting in new policy growth of 6.2% last year.
Source: LexisNexis Risk Solutions Insurance Demand Meter
Get the latest auto insurance shopping trend data in our quarterly report.
Insurance Demand Meter
Driving behavior continues to change dramatically
As miles driven returned to 2019 levels in 2023, moving and non-moving violations returned as well. In fact, all driving violations have increased 4% from 2022 to 2023. Speeding, distracted driving and DUIs all increased year over year, resulting in escalating risk profiles. Get additional details in this blog post .
Both major and minor speeding violations continue to rise, consistent with the post-pandemic trend.
Unlike other violations, DUIs take longer to move through the court system, so they can be a lagging metric. Comparing the first six months of 2022 to the first six months of 2023, DUI violations increase 8%. Our latest driving violations blog dives into current unsafe driving trends.
Distracted Driving
Distracted driving violations continue to rise as people return to the roads. Younger drivers continue to be the more susceptible problematic age group when it comes to increases in distracted driving. This increase in violations, plus Gen Z’s inexperience compared to other generations, has implications for both personal lines and commercial lines insurers. Read more in our latest blog article .
Access the Report
Download previous lexisnexis® u.s. auto insurance trends reports.
2023 Auto Insurance Trends Report
2022 auto insurance trends report, 2021 auto insurance trends report .
Academia.edu no longer supports Internet Explorer.
To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to upgrade your browser .
Enter the email address you signed up with and we'll email you a reset link.
- We're Hiring!
- Help Center
A Study on Customer Awareness on Car Insurance Policies with Special Reference to
Purpose: The purpose of this study is to understand the customer awareness on car insurance policies with special reference to United India Insurance with the important element to improve the customer awareness towards insurance policies based on literature review and case study of successful vehicle Insurance Company. This study mainly focused on customer's awareness and satisfaction level on the car insurance policies offered by the company. Research Design: This research study is mainly based on the method of probability sampling with random sampling techniques, this research study is conducted within shivamogga city with the sample size of 150 respondents from the Primary data which is collected through structured questionnaire as a sample tool for the information's assembly, secondary data is collected by the magazine, journals of the marketing, articles and books, Findings: From the study came to know that respondents or policy holders are not aware about the terms and conditions, procedures of claiming during the time of damage or loss of the insurance policy offered by the company. Results: United India Insurance Corporation is a well-known insurance organization in the field of vehicle insurance Business which is a leading insurance sectors in providing service to the customers and customers are well satisfied with the price of the insurance policies offered by the united India insurance organization to the customers. Conclusion: From this study it is cleared that most of the policy holders are not aware about the procedures, terms and conditions, policies premium calculation procedure based on vehicle ID value, age, model etc. The concept of car insurance policies is very much needed aspects to the people who have owned a car, having car insurance policies makes the customers feel protected from the loss or damage if caused by the accident.
Related Papers
Journal of emerging technologies and innovative research
Josephine Stella A.
Motor insurance contributes to one third of the premium income for the General Insurance industry in India. The growth of the economy and consequently, the standard of living of the people, further supported by the increased choice for the customer and entry of large number of automobile players led to a sharp increase in motor insurance. The main aim of the motor insurance is to protect the people from the loss arising out of accident. It covers loss made vehicle. The awareness of the people towards Insurance is low in India generally it is very difficult to create the buying attitude among the prospective buyers towards the different kinds of insurance. The General Insurance Corporation finds it difficult to identify and to make the clients believe the concepts of Insurance Policy. At the same time, the policy will be valued for only one year. The lack of insurance awareness is the main problem in general insurance particularly in motor insurance. Vehicle owners buy it only on the...
International Journal of Research in Commerce and Management
Dhiraj Jain
vikas kumar gautam
International Journal of Management, Technology, and Social Sciences (IJMTS)
Srinivas Publication , Swati Basu Ghose
The primary purpose of vehicle insurance is to cover the vehicle against damage, personal injury, and third-party liability. In addition to this, some insurance companies also provide value-added services such as roadside assistance and other services in return of the amount called as premium which attracts a large number of customers. However, our study shows that vehicle owners give maximum importance to the cost of insurance in terms of the annual premium. Primary data has been collected through questionnaire and analysed to ascertain about the factors responsible for taking out vehicle insurance, choice between private and public sector insurance companies, preferred insurance companies among the major players in the field, factors that play a role in the customers' choice of a particular insurance company, customers' opinion about the affordability of the premium to be paid, customers' satisfaction with their chosen company, whether customers consider fast and efficient service as a deciding factor, and whether the brand value of the company plays a role in the customers' choice.
Turkish Online Journal of Qualitative Inquiry (TOJQI) Volume 12, Issue 2, March 2021: 801-815
veera venkat satyanarayana penumarthi
An effort is being made in this study to show how Vijayawada customers see insurance services. Respondents to a five-point Likert scale questionnaire were used to compile the data for this research. More than 377 people were surveyed to determine their degree of knowledge and attitude about insurance services. According to a new study, Vijayawada customers' perceptions about insurance services are strongly influenced by socioeconomic and demographic factors. Insurance businesses in Vijayawada may use the results of this research as a basis for developing marketing plans that include socio-demographic and economic factors.
Dharmesh Motwani
IJIRIS:: AM Publications,India
IJIRIS International Journal of Innovative Research in Information Security
The journey of new India Insurances scheme has stated 17th century England. Insurances are the co– operative device who distribute the loss caused by a particular risk.When any insurances company face any types of mismanagement, they should look for a market for that policy instead of constantly lying to the public or with their clients. Market competition brings decrease in price of the insurances company and increase in the quality but customer play their part according to their views. In the unpredictable society insurances paly a secure part but customers face many other problems. In this paper we will discussed about the problems faced by the customers and also their reasons why many people don’t have faith in the insurances company. The paper is focused on the problems faces by the customer in insurances sector.
Pranjal Bezborah
Sathishkumar Ramasamy
the present study analyzes the attitudes of policyholders of Life Insurance Corporation of India with special reference to Tiruchirappalli district, the data were collected and analysed as per the requirement of the study. The primary data were collected from the respondents through interview schedule in June 2011 to March 2012. The study has adopted proportionate stratified random sampling method for selecting 500 respondents. The results revealed the fact that the factors, age, education, marital status, family size, number of earning members, income and awareness have influenced the level of attitude of the policyholders. Whereas the factors like sex, occupation and patronage mentality did not influence the level of attitude.
IP innovative publication pvt ltd
IP Innovative Publication Pvt. Ltd.
With the increase in risk there is need of insurance to bear the losses. Insurance is the instrument used as the financial protection against various contingency. This paper examines the customer perception towards the General Insurance. A study had been conducted at Gwalior region with the sample of 200 respondents to find out the perception of the customer (policyholders). In this context, the respondents’ opinion on the various related statements were collected with a 5 point scaling. Reliability, Factor analysis, multivariate technique had been applied on the data. The result concluded that loyalty, transparency, proficiency, reliable and convenient services are the five factors from the 18 statements on the basis of the expectation of the customers. This study signifies that various customer had different expectation from the insurance company in the studied area.
Loading Preview
Sorry, preview is currently unavailable. You can download the paper by clicking the button above.
RELATED PAPERS
Dr.Dhiraj Jain
iaeme iaeme
Carmelo Panepinto
Murangirwa Festus
International Journal of Engineering and Advanced Technology
Srimannarayana Gajula
Ahmed Salman Syed
SHS Web of Conferences
Sesilya Kempa
Mohammad Jamal Hossain
IOSR Journal of Business and Management
Festus Epetimehin
International Journal of Law Management & Humanities (IJLMH) A peer-reviewed, HeinOnline, MANUPATRA, Google Scholar & 23 databases Indexed Int'l Journal with IF of 6.530.
Jayshree Singh
Velmurugan Ramasamy
As-Syirkah: Islamic Economic & Financial Journal
Nurul Jannah
Journal of Asian Finance, Economics and Business
adinoto nursiana
Arti Sharma
Sarang S Bhola
PARIPEX INDIAN JOURNAL OF RESEARCH
Chitralekha Dhadhal
Restaurant Business
Bhavna Pathak
International Journal of Engineering & Technology
Dr. Arun Vijay
Nepalese Journal of Insurance and Social Security
Aayush Poudel
Lakshmi Sivaramakrishnan
The Journal of Risk Management
Bonny Bagenda
Sunil Kumar
IAEME PUBLICATION
IAEME Publication
Publishing India Group
https://www.ijrrjournal.com/IJRR_Vol.6_Issue.8_Aug2019/Abstract_IJRR0041.html
International Journal of Research & Review (IJRR)
RELATED TOPICS
- We're Hiring!
- Help Center
- Find new research papers in:
- Health Sciences
- Earth Sciences
- Cognitive Science
- Mathematics
- Computer Science
- Academia ©2024
You might be using an unsupported or outdated browser. To get the best possible experience please use the latest version of Chrome, Firefox, Safari, or Microsoft Edge to view this website. |
- Best Pet Insurance Companies
Best Pet Insurance Companies Of August 2024
Expert Reviewed
Updated: Aug 5, 2024, 2:03pm
We evaluated 13 policies and found that Pets Best is the best pet insurance company.
A solid pet insurance plan covers major medical emergencies like getting struck by a vehicle, cancer treatment, torn ligaments and swallowed foreign objects. It also covers common health problems like digestive issues and ear infections. You’ll find that pet insurance companies offer a wide variety of coverage types and benefits.
To help you narrow your options, we analyzed the best pet insurance for discounts, superior benefits, short waiting periods and more.
- Cheapest Pet Insurance Companies
- Best Pet Insurance Wellness Plans For Routine Care
- Best Emergency Pet Insurance
- Compare Pet Insurance Quotes
Summary: Ratings of Pet Insurance Companies
Compare the best pet insurance companies, how much does pet insurance cost, how to choose the right pet insurance, methodology, best pet insurance frequently asked questions (faqs).
IMAGES
COMMENTS
Furthermore, as reported in this research, the analysis of keyword co-occurrence and its subsequent network visualisation contributed to highlighting the knowledge framework relevant to telematics-based automotive insurance. The knowledge structure of car insurance studies using telematics was mapped using keyword co-occurrence analysis, and ...
This study explored machine learning algorithms to det ect fraudulent. vehicle insurance claims. The r esearch evaluated AdaBoost, XGboostNB, SVM, LR, D T, ANN, and RF. AdaBoost and XGBoost classi ...
Abstract. With the continuous development of machine learning, enterprises using machine learning methods to mine potential data information has become a hot topic in the research of major insurance companies. In this paper, the features of auto insurance data are analyzed, and the most important features affecting auto renewal are mined.
DOI: 10.4236/gep.2016.412002 December 1, 2016. Car Insurance Plans Could Make a Society Safer. Mohammad Zand, Amir Samimi, Khashayar Khavarian. Civil Engineering Department, Shar if University of ...
Denneberg first proposed the Poisson-gamma model to study the frequency of nonhomogeneous insurance policy claims and obtained good fitting results in empirical research on auto insurance . The generalized linear model (GLM) is a widely accepted model for premium ratemaking of automobile insurance in recent decades.
The purpose of this paper is to survey the automobile insurance fraud detection literature in the past 31 years (1990-2021) and present a research agenda that addresses the challenges and opportunities artificial intelligence and machine learning bring to car insurance fraud detection.,Content analysis methodology is used to analyze 46 peer ...
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 ...
The study of actuarial fairness in auto insurance has been an important issue in the decision making of rate regulation. Risk classification and estimating risk relativities through statistical modeling become essential to help achieve fairness in premium rates. ... University of Texas Law, Law and Econ Research Paper No. E574. Available online ...
Many past papers have focused on recommender systems for insurance companies where one of a small number of insurance products is offered. In [17, 18], they used historical data of existing and past customers to determine the most suitable policy for a new customer.In this case, a relatively small number of insurance products are available, and hence, the number of customers who have been ...
Purpose The purpose of this paper is to survey the automobile insurance fraud detection literature in the past 31 years (1990-2021) and present a research agenda that addresses the challenges ...
Top 5 Auto Insurance Trends To Watch. The annual LexisNexis® Risk Solutions U.S. Auto Insurance Trends Report explores key trends from the previous year and offers insights to help insurers make more informed business decisions. This year, we identified five trends impacting U.S. consumer auto insurance shopping, claims, driving violations and ...
1.1 IMPACT OF TECHNOLOGICAL ADVANCES IN AUTO INSURANCE CLAIMS. Auto insurance claims will be impacted by two distinct forms of technological advances: enhanced safety features, and autonomous vehicles. Regarding the development of enhanced safety measures, the panel members noted:
Insurance Research Council (IRC). In addition to documenting that auto insurance has become more affordable for both the nation as a whole and across states, the study also showed that the ... Auto Insurance Expenditure to Income Ratio - Decade Average 1990s average 2000s average 2010s average. Author:
the insurance policy and the outcome of applying optimal premium rates in several customer segments are shown. I. Introduction The sensitivity of car insurance customers to price and how this affects their retention has been a subject of intense analysis in the insurance market research literature. Different
most insurance companies experience great loss as far as car insurance as shown in Figure 1, one of the main challenges face the insurance companies nowadays, is to define a proper premium for each risk represented by those customers [4], the majority of insurance companies keep the data on the history of its operations in a data warehouse
yers.Defining Discrimination in InsuranceBy Kudakwashe F. Chibanda, FCASExecutive SummaryThis research paper is designed to introduce various terms used in defining discrimination by stakeholders in. the insurance industry (regulators, consumer advocacy groups, actuaries and insurers, etc.). The paper defines protected class, unfair discriminat.
IJIRST -International Journal for Innovative Research in Science & Technology| Volume 5 | Issue 5 | October 2018 ISSN (online): 2349-6010 A Study on Customer Awareness on Car Insurance Policies with Special Reference to United India Insurance, Shivamogga Dr (HC) D. M. Arvind Mallik Assistant Professor PGDMS, PESITM, Shivamogga-577201 ...
Whether you're focused on price, claims handling, or customer service, we've researched insurers nationwide to provide our best-in-class picks for car insurance coverage. Read our free expert ...
See how to save on car insurance. ... The Motley Fool Ascent's research recently found that 20% of drivers have never shopped for cheaper car insurance, and only 26% shop for price quotes every ...
Car insurance costs went up by over 20% per year, according to recent survey data. Many drivers are reeling from the rapid increase in car insurance premiums.
A recent study from Pew Research reveals goods and services that have seen the highest price increases since 2020. Car insurance makes the top 10 list of highest price increases since 2020. From ...
Accuracy, Precision, Recall, and F - measure. The prediction. accuracy of t he model is capable of predicting the motor. in surance claim status with 98.36% and 98.17% by RF and SVM. classifiers ...
Even as inflation has eased, car insurance rates are rising by double digits. But drivers have some options for reining in premiums. According to the Bureau of Labor Statistics, auto insurance ...
How to choose the best pet insurance for your furry friends? Compare Pets Best, Embrace and Paw Protect, the top-rated plans by Forbes Advisor, and see how they cover accidents, illnesses, and more.
Sheremetyevo International Airport (SVO/UUEE) is an international airport located in Khimki, Moscow Oblast.Sheremetyevo serves as the main hub for Russian flag carrier Aeroflot and its branch Rossiya Airlines, Nordwind Airlines or Ural Airlines. The product is equipped with an automatic installer, which means that the scenery will be ...
Traktir. 10. $$ - $$$. Suponevo Tourism: Tripadvisor has reviews of Suponevo Hotels, Attractions, and Restaurants making it your best Suponevo resource.
The first step to buy car insurance online is thorough research. Start by visiting the websites of various insurance companies and using comparison tools to evaluate different policies. These tools allow you to compare coverage options, benefits, and premiums side-by-side, making it easier to find a policy that suits your needs and budget. ...
Low deductible car insurance policies allow you to use the policy with very little out-of-pocket cost per claim, sometimes as little as $0. ... Research has found a correlation between credit ...
Public transport stop: Старая Руза, Moscow and Moscow Oblast. Public transport that stops here: buses: 21, 21/62 and 3 more — Yandex Maps.
Automobile Insurance F raud Detection. Mark Anthony Caruana 1 and Liam Grec h. 1 Department of Statistics and Operations Research, Faculty of Science, University. of Malta, Msida, Malta. (E-mail ...