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.
|Publication status||Published - 2022 Dec|
Bibliographical noteFunding Information:
This study was funded by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2020R1A2C2005026).
This study was funded by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2020R1A2C2005026). Hyunwoo Woo is grateful for financial support from Hyundai Motor Chung Mong-Koo Foundation.
© 2022, The Author(s).
All Science Journal Classification (ASJC) codes
- Management of Technology and Innovation