Abstract
AbstractAlthough 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.
Funder
National Research Foundation of Korea
Publisher
Springer Science and Business Media LLC
Subject
Management of Technology and Innovation,Finance
Reference79 articles.
1. Akerlof GA, Kranton RE (2000) Economics and identity. Q J Econ 115(3):715–753
2. Armstrong SJ, Cools E, Sadler-Smith E (2012) Role of cognitive styles in business and management: Reviewing 40 years of research. Int J Manag Rev 14(3):238–262
3. Arráiz I, Bruhn M, Stucchi R (2017) Psychometrics as a tool to improve credit information. World Bank Econ Rev 30(Supplement 1):S67–S76
4. Belleflamme P, Omrani N, Peitz M (2015) The economics of crowdfunding platforms. Inf Econ Policy 33:11–28
5. Blanco A, Pino-Mejías R, Lara J, Rayo S (2013) Credit scoring models for the microfinance industry using neural networks: evidence from Peru. Expert Syst Appl 40(1):356–364
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