Comparison of Borrower Default Factors in Online Lending
The factors describing the P2P borrower late payments and defaults are analyzed in the paper. Credit scoring and credit rating techniques are developed and used by finance institutions, but the features of online lending encourages to apply new practices in order to develop the decision support patterns for online lenders that are not professional investors. P2P platforms use credit scoring usually based on third party calculations, but they may be improved using wider soft information sources. The credit risk valuation of online borrowers is relatively new research area, where hard and soft information is used and assessed with different statistical methods, including the big data analysis. The paper aims to define the factors of online borrower late payments by systemizing the recent research findings and comparing them with results got from Lithuanian P2P platform data. The groups of factors researched are borrower and loan characteristics, borrower assessment and creditworthiness. The main findings allow to form specific propositions for lender decision support pattern suggesting the factors explaining the default: lower credit ratings and higher interest rates; greater loan amount and loan purpose for business, consolidation, home improvement and other; borrower indebtedness, employment length, age.
KeywordsCredit risk Credit score Default Borrower assessment Online lending Peer to peer lending
- Chen, D., & Han, C. A. (2012). Comparative study of P2P lending in the USA and China. Journal of Internet Banking and Commerce, 17(2), 1–15.Google Scholar
- Everett, C. R. (2015). Group membership, relationship banking and loan default risk: The case of online social lending. Journal of Banking and Finance Review, 7(2) [online]. Accessed November 11, 2016, from https://ssrn.com/abstract=1114428
- Freedman, S., & Jin, G. Z. (2011). Learning by doing with asymmetric information: Evidence from Prosper.com (NBER Working Paper No. 16855).
- Greiner, M. E., & Wang, H. (2009). The role of social capital in people-to-people lending marketplaces. Proceedings of International Conference of Information Systems (ICIS), 29 [online]. Accessed December 15, 2016, from http://aisel.aisnet.org/icis2009/29
- Iyer, R., Khwaja, A. I., Luttmer, E. R. P., & Shue, K. (2009). Screening in new credit markets: Can individual lenders infer borrower credit worthiness in peer-to-peer lending? (Working paper) [online]. Accessed December 15, 2016, from http://ssrn.com/abstract=1570115
- Kocenda, E., & Vojtek, M. (2009). Default predictors and credit scoring models for retail banking (CESifo Working Paper No. 2862, 53 p) [online]. Accessed June 10, 2016, from https://ssrn.com/abstract=1519792
- Li, Z., Yao, X., Wen, Q., & Yang, W. (2016). Prepayment and default of consumer loans in online lending [Online]. Accessed December 15, 2016, from https://ssrn.com/abstract=2740858
- Miller, S. (2014). Risk factors for consumer loan default: A censored quantile regression analysis [online]. Accessed June 10, 2016, from http://www-personal.umich.edu/~mille/riskfactors.pdf
- Pope, D. G., & Sydnor, J. R. (2011). What’s in a picture? Evidence of discrimination from Prosper.com. The Journal of Human Resources, 46(1), 53–92.
- Railiene, G., & Ivaskeviciute, L. (2016). Information quality for P2P platform selection in global environment. Globalization and Its Socio-Economic Consequences, 16th International Scientific Conference Proceedings [online]. Accessed December 15, 2016, from http://ke.uniza.sk/sites/default/files/content_files/part_iv_final_2.pdf
- WEF. (2015). The future of financial services. How disruptive innovations are reshaping the way financial services are structured, provisioned and consumed. World Economic Forum (WEF), in collaboration with Deloitte, Final Report [online]. Accessed June 10, 2016, from http://www3.weforum.org/docs/WEF_The_future__of_financial_services.pdf
- Yan, J., Yu, W., & Zhao, J. L. (2015). How signaling and search costs affect information asymmetry in P2P lending: The economics of big data. Journal of Financial Innovation, 1(19), 1–11.Google Scholar