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Biological and Psychobehavioral Correlates of Credit Scores and Automobile Insurance Losses: Toward an Explication of Why Credit Scoring Works

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  • Patrick L. Brockett
  • Linda L. Golden

Abstract

The most important new development in the past two decades in the personal lines of insurance may well be the use of an individual's credit history as a classification and rating variable to predict losses. However, in spite of its obvious success as an underwriting tool, and the clear actuarial substantiation of a strong association between credit score and insured losses over multiple methods and multiple studies, the use of credit scoring is under attack because there is not an understanding of why there is an association. Through a detailed literature review concerning the biological, psychological, and behavioral attributes of risky automobile drivers and insured losses, and a similar review of the biological, psychological, and behavioral attributes of financial risk takers, we delineate that basic chemical and psychobehavioral characteristics (e.g., a sensation‐seeking personality type) are common to individuals exhibiting both higher insured automobile loss costs and poorer credit scores, and thus provide a connection which can be used to understand why credit scoring works. Credit scoring can give information distinct from standard actuarial variables concerning an individual's biopsychological makeup, which then yields useful underwriting information about how they will react in creating risk of insured automobile losses.

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  • Patrick L. Brockett & Linda L. Golden, 2007. "Biological and Psychobehavioral Correlates of Credit Scores and Automobile Insurance Losses: Toward an Explication of Why Credit Scoring Works," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 74(1), pages 23-63, March.
  • Handle: RePEc:bla:jrinsu:v:74:y:2007:i:1:p:23-63
    DOI: 10.1111/j.1539-6975.2007.00201.x
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    References listed on IDEAS

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    1. Lucia Dunn & TaeHyung Kim, 1999. "Empirical Investigation of Credit Card Default," Working Papers 99-13, Ohio State University, Department of Economics.
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    Cited by:

    1. Charles M. Nyce & Patrick Maroney, 2011. "Are Territorial Rating Models Outdated in Residential Property Insurance Markets? Evidence From the Florida Property Insurance Market," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 14(2), pages 201-232, September.
    2. Richard A. Derrig & Sharon Tennyson, 2011. "The Impact of Rate Regulation on Claims: Evidence From Massachusetts Automobile Insurance," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 14(2), pages 173-199, September.
    3. Kamil Gala & Karolina Kolak, 2015. "The use of credit data for risk classification in automobile insurance," Collegium of Economic Analysis Annals, Warsaw School of Economics, Collegium of Economic Analysis, issue 37, pages 73-98.
    4. Wei‐Jin Wu & Chu‐Shiu Li & Sheng‐Chang Peng, 2020. "The relationships between vehicle characteristics and automobile accidents," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 23(4), pages 331-377, December.
    5. Alma Cohen & Peter Siegelman, 2010. "Testing for Adverse Selection in Insurance Markets," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 77(1), pages 39-84, March.
    6. David A. Cather, 2020. "Reconsidering insurance discrimination and adverse selection in an era of data analytics," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 45(3), pages 426-456, July.
    7. David A. Cather, 2018. "Cream Skimming: Innovations in Insurance Risk Classification and Adverse Selection," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 21(2), pages 335-366, September.
    8. Jing Ai & Lin Zhao & Wei Zhu, 2016. "Contracting with Present-Biased Consumers in Insurance Markets," The Geneva Papers on Risk and Insurance Theory, Springer;International Association for the Study of Insurance Economics (The Geneva Association), vol. 41(2), pages 107-148, September.
    9. Jing Ai & Patrick L. Brockett & Tianyang Wang, 2017. "Optimal Enterprise Risk Management and Decision Making With Shared and Dependent Risks," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 84(4), pages 1127-1169, December.
    10. Hyunwoo Woo & So Young Sohn, 2022. "A credit scoring model based on the Myers–Briggs type indicator in online peer-to-peer lending," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-19, December.
    11. Tian , Xiaoguang & Prybutok, Victor & Mirzaei, Fouad & Dinulescu, Catalin C., 2020. "Millennials Acceptance of Insurance Telematics: An Integrative Empirical Study," American Business Review, Pompea College of Business, University of New Haven, vol. 23(1), pages 156-181, May.

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