IDEAS home Printed from https://ideas.repec.org/a/spr/fininn/v8y2022i1d10.1186_s40854-022-00347-4.html
   My bibliography  Save this article

A credit scoring model based on the Myers–Briggs type indicator in online peer-to-peer lending

Author

Listed:
  • Hyunwoo Woo

    (Yonsei University)

  • So Young Sohn

    (Yonsei University)

Abstract

Although psychometric features have been considered for alternative credit scoring, they have not yet been applied to peer-to-peer (P2P) lending because such information is not available on platforms. This study proposed an alternative credit scoring model for P2P lending by extracting typical personality types inferred from the borrowers’ job category. We projected a virtual space of borrowers by using the affinity matrix based on the Myers–Briggs type indicator (MBTI) that fits each job category. Applying the distance in this space to Lending Club data, we used locally weighted logistic regression to vary the coefficients of the variables, which affect loan repayments, with each MBTI type for predicting the default probability. We found that each MBTI type’s credit scoring model has different significant variables. This study provides insights into breakthroughs in developing alternative credit scoring for P2P lending.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:fininn:v:8:y:2022:i:1:d:10.1186_s40854-022-00347-4
    DOI: 10.1186/s40854-022-00347-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1186/s40854-022-00347-4
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1186/s40854-022-00347-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Guo, Yanhong & Zhou, Wenjun & Luo, Chunyu & Liu, Chuanren & Xiong, Hui, 2016. "Instance-based credit risk assessment for investment decisions in P2P lending," European Journal of Operational Research, Elsevier, vol. 249(2), pages 417-426.
    2. Douglas Lamdin, 2008. "Does Consumer Sentiment Foretell Revolving Credit Use?," Journal of Family and Economic Issues, Springer, vol. 29(2), pages 279-288, June.
    3. Nigmonov, Asror & Shams, Syed & Alam, Khorshed, 2022. "Macroeconomic determinants of loan defaults: Evidence from the U.S. peer-to-peer lending market," Research in International Business and Finance, Elsevier, vol. 59(C).
    4. Belleflamme, Paul & Omrani, Nessrine & Peitz, Martin, 2015. "The economics of crowdfunding platforms," Information Economics and Policy, Elsevier, vol. 33(C), pages 11-28.
    5. Oberlechner, Thomas & Hocking, Sam, 2004. "Information sources, news, and rumors in financial markets: Insights into the foreign exchange market," Journal of Economic Psychology, Elsevier, vol. 25(3), pages 407-424, June.
    6. Joël Peress, 2004. "Wealth, Information Acquisition, and Portfolio Choice," The Review of Financial Studies, Society for Financial Studies, vol. 17(3), pages 879-914.
    7. Zhang, Bryan & Wardrop, Robert & Rau, Raghavendra & Gray, Mia, 2015. "Moving mainstream: benchmarking the European alternative finance market," Journal of Financial Perspectives, EY Global FS Institute, vol. 3(3), pages 60-76.
    8. Cuiqing Jiang & Zhao Wang & Ruiya Wang & Yong Ding, 2018. "Loan default prediction by combining soft information extracted from descriptive text in online peer-to-peer lending," Annals of Operations Research, Springer, vol. 266(1), pages 511-529, July.
    9. Watson, John J., 2003. "The relationship of materialism to spending tendencies, saving, and debt," Journal of Economic Psychology, Elsevier, vol. 24(6), pages 723-739, December.
    10. 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.
    11. Luigi Guiso & Paola Sapienza & Luigi Zingales, 2013. "The Determinants of Attitudes toward Strategic Default on Mortgages," Journal of Finance, American Finance Association, vol. 68(4), pages 1473-1515, August.
    12. Julapa Jagtiani & Catharine Lemieux, 2019. "The roles of alternative data and machine learning in fintech lending: Evidence from the LendingClub consumer platform," Financial Management, Financial Management Association International, vol. 48(4), pages 1009-1029, December.
    13. Jimenez, Gabriel & Saurina, Jesus, 2004. "Collateral, type of lender and relationship banking as determinants of credit risk," Journal of Banking & Finance, Elsevier, vol. 28(9), pages 2191-2212, September.
    14. Huan Tang, 2019. "Peer-to-Peer Lenders Versus Banks: Substitutes or Complements?," The Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 1900-1938.
    15. Wang, Yao & Drabek, Zdenek & Wang, Zhengwei, 2022. "The role of social and psychological related soft information in credit analysis: Evidence from a Fintech Company," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 96(C).
    16. Riza Emekter & Yanbin Tu & Benjamas Jirasakuldech & Min Lu, 2015. "Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending," Applied Economics, Taylor & Francis Journals, vol. 47(1), pages 54-70, January.
    17. José María Liberti & Mitchell A Petersen, 2019. "Information: Hard and Soft," The Review of Corporate Finance Studies, Society for Financial Studies, vol. 8(1), pages 1-41.
    18. Tokunaga, Howard, 1993. "The use and abuse of consumer credit: Application of psychological theory and research," Journal of Economic Psychology, Elsevier, vol. 14(2), pages 285-316, June.
    19. Beatriz Fernández & Teresa Garcia‐Merino & Rosa Mayoral & Valle Santos & Eleuterio Vallelado, 2011. "Herding, information uncertainty and investors' cognitive profile," Qualitative Research in Financial Markets, Emerald Group Publishing Limited, vol. 3(1), pages 7-33, April.
    20. George A. Akerlof & Rachel E. Kranton, 2000. "Economics and Identity," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 115(3), pages 715-753.
    21. Insler, Michael & Compton, James & Schmitt, Pamela, 2016. "The investment decisions of young adults under relaxed borrowing constraints," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 64(C), pages 106-121.
    22. Yufei Xia & Lingyun He & Yinguo Li & Nana Liu & Yanlin Ding, 2020. "Predicting loan default in peer‐to‐peer lending using narrative data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 260-280, March.
    23. Gathergood, John, 2012. "Self-control, financial literacy and consumer over-indebtedness," Journal of Economic Psychology, Elsevier, vol. 33(3), pages 590-602.
    24. Kuhnen, Camelia M. & Knutson, Brian, 2011. "The Influence of Affect on Beliefs, Preferences, and Financial Decisions," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 46(3), pages 605-626, June.
    25. Zanin, Luca, 2020. "Combining multiple probability predictions in the presence of class imbalance to discriminate between potential bad and good borrowers in the peer-to-peer lending market," Journal of Behavioral and Experimental Finance, Elsevier, vol. 25(C).
    26. Nigel Nicholson & Emma Soane & Mark Fenton-O'Creevy & Paul Willman, 2005. "Personality and domain-specific risk taking," Journal of Risk Research, Taylor & Francis Journals, vol. 8(2), pages 157-176, March.
    27. Haomin Wang & Gang Kou & Yi Peng, 2021. "Multi-class misclassification cost matrix for credit ratings in peer-to-peer lending," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 72(4), pages 923-934, March.
    28. Carlos Serrano-Cinca & Begoña Gutiérrez-Nieto & Luz López-Palacios, 2015. "Determinants of Default in P2P Lending," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-22, October.
    29. Jiaqi Yan & Wayne Yu & J. Leon Zhao, 2015. "How signaling and search costs affect information asymmetry in P2P lending: the economics of big data," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 1(1), pages 1-11, December.
    30. Wang, Lili & Lu, Wei & Malhotra, Naresh K., 2011. "Demographics, attitude, personality and credit card features correlate with credit card debt: A view from China," Journal of Economic Psychology, Elsevier, vol. 32(1), pages 179-193, February.
    31. Gulnur Muradoglu & Nigel Harvey, 2012. "Behavioural finance: the role of psychological factors in financial decisions," Review of Behavioral Finance, Emerald Group Publishing Limited, vol. 4(2), pages 68-80, November.
    32. Irani Arráiz & Miriam Bruhn & Rodolfo Stucchi, 2017. "Psychometrics as a Tool to Improve Credit Information," The World Bank Economic Review, World Bank, vol. 30(Supplemen), pages 67-76.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Štefan Lyócsa & Petra Vašaničová & Branka Hadji Misheva & Marko Dávid Vateha, 2022. "Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-21, December.
    2. Ligang Zhou & Chao Ma, 2023. "A Comparison of Different Rules on Loans Evaluation in Peer-to-Peer Lending by Gradient Boosting Models Under Moving Windows with Two Timestamps," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1481-1504, December.
    3. Samuel Ribeiro-Navarrete & Juan Piñeiro-Chousa & M. Ángeles López-Cabarcos & Daniel Palacios-Marqués, 2022. "Crowdlending: mapping the core literature and research frontiers," Review of Managerial Science, Springer, vol. 16(8), pages 2381-2411, November.
    4. Croux, Christophe & Jagtiani, Julapa & Korivi, Tarunsai & Vulanovic, Milos, 2020. "Important factors determining Fintech loan default: Evidence from a lendingclub consumer platform," Journal of Economic Behavior & Organization, Elsevier, vol. 173(C), pages 270-296.
    5. Chen, Pei-Fen & Lo, Shihmin & Tang, Hai-Yuan, 2022. "What if borrowers stop paying their loans? Investors’ rates of return on a peer-to-peer lending platform," International Review of Economics & Finance, Elsevier, vol. 77(C), pages 359-377.
    6. Benjamin Käfer, 2016. "Peer-to-Peer Lending – A (Financial Stability) Risk Perspective," MAGKS Papers on Economics 201622, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    7. Xia, Yufei & Zhao, Junhao & He, Lingyun & Li, Yinguo & Yang, Xiaoli, 2021. "Forecasting loss given default for peer-to-peer loans via heterogeneous stacking ensemble approach," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1590-1613.
    8. Serena Gallo, 2021. "Fintech platforms: Lax or careful borrowers’ screening?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-33, December.
    9. Rendall, Stella & Brooks, Chris & Hillenbrand, Carola, 2021. "The impacts of emotions and personality on borrowers’ abilities to manage their debts," International Review of Financial Analysis, Elsevier, vol. 74(C).
    10. Wang, Yao & Drabek, Zdenek & Wang, Zhengwei, 2022. "The role of social and psychological related soft information in credit analysis: Evidence from a Fintech Company," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 96(C).
    11. Kowalewski, Oskar & Pisany, Paweł, 2022. "Banks' consumer lending reaction to fintech and bigtech credit emergence in the context of soft versus hard credit information processing," International Review of Financial Analysis, Elsevier, vol. 81(C).
    12. Xueru Chen & Xiaoji Hu & Shenglin Ben, 2021. "How do reputation, structure design and FinTech ecosystem affect the net cash inflow of P2P lending platforms? Evidence from China," Electronic Commerce Research, Springer, vol. 21(4), pages 1055-1082, December.
    13. Xie, Xueyan & Zhu, Xiaoyang, 2022. "FinTech and capital allocation efficiency: Another equity-efficiency dilemma?," Global Finance Journal, Elsevier, vol. 53(C).
    14. Käfer Benjamin, 2018. "Peer-to-Peer Lending – A (Financial Stability) Risk Perspective," Review of Economics, De Gruyter, vol. 69(1), pages 1-25, April.
    15. Zhao Wang & Cuiqing Jiang & Huimin Zhao, 2022. "Know Where to Invest: Platform Risk Evaluation in Online Lending," Information Systems Research, INFORMS, vol. 33(3), pages 765-783, September.
    16. Yufei Xia & Lingyun He & Yinguo Li & Nana Liu & Yanlin Ding, 2020. "Predicting loan default in peer‐to‐peer lending using narrative data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 260-280, March.
    17. Bertsch, Christoph & Hull, Isaiah & Qi, Yingjie & Zhang, Xin, 2020. "Bank misconduct and online lending," Journal of Banking & Finance, Elsevier, vol. 116(C).
    18. Gaigalienė Asta & Česnys Dovydas, 2018. "Determinants of Default in Lithuanian Peer-To-Peer Platforms," Management of Organizations: Systematic Research, Sciendo, vol. 80(1), pages 19-36, December.
    19. Jin, Ming & Yin, Mingmei & Chen, Zhongfei, 2021. "Do investors prefer borrowers from high level of trust cities? Evidence from China’s P2P market," Research in International Business and Finance, Elsevier, vol. 58(C).
    20. Mousumi Munmun & Dongli Zhang & Charles C. Luo, 2024. "Peer-to-Peer Lending Performance Improvement: Learn from Lean Principles," International Journal of Business and Management, Canadian Center of Science and Education, vol. 19(1), pages 101-101, February.

    More about this item

    Keywords

    Alternative credit scoring; Behavioral finance; Credit scoring; Locally weighted logistic regression; MBTI; P2P lending;
    All these keywords.

    JEL classification:

    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • E41 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Demand for Money
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:fininn:v:8:y:2022:i:1:d:10.1186_s40854-022-00347-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.