Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chandler, J., & Chen, S. (2015). Prosumer motivations in service experiences. Journal of Service Theory and Practice, 25(2), 220–239.
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.
Chen, M., & Huang, S. (2003). Credit scoring and rejected instances reassigning through evolutionary computation techniques. Journal of Expert Systems with Applications, 24(4), 433–441.
Chen, D., Lai, F., & Lin, Z. (2014). A trust model for online peer-to peer lending: A lender’s perspective. Journal of Information Technology and Management, 15, 239–254.
Eletter, S. F., Yaseen, S. G., & Elrefae, G. A. (2010). Neuro-based artificial intelligence model for loan decisions. American Journal of Economics and Business Administration, 2(1), 27–34.
Emekter, R., Tu, Y., Jirasakuldech, B., & Lu, M. (2015). Evaluating credit risk and loan performance in online peer-to peer (P2P) lending. Journal of Applied Economics, 47(1), 54–70.
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. (2008). Do social networks solve information problems for peer-to-peer lending? Evidence from Prosper.com [online]. Accessed November 28, 2016, from http://ssrn.com/abstract=1304138
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
Guo, Y., Zhou, W., Luo, C., & Xiong, H. (2016). Instance-based credit risk assessment for investment decisions. European Journal of Operational Research, 249, 417–426.
Herzenstein, M., Dholakia, U., & Andrewsc, R. (2011). Strategic herding behavior in peer-to-peer loan auctions. Journal of Interactive Marketing, 25, 27–36.
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
Lin, M., Prabhala, N. R., & Viswanathan, S. (2013). Judging borrowers by the company they keep: Friendship networks and information asymmetry in online peer-to-peer lending. Journal of Management Science, 59(1), 17–35.
Malhotra, R., & Malhotra, D. K. (2003). Evaluating consumer loans using neural networks. Omega, 31(2), 83–96.
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
Serrano-Cinca, C., & Gutierrez-Nieto, B. (2016). The use of profit scoring as an alternative to credit scoring systems in peer-to-peer (P2P) lending. Journal of Decision Support Systems, 89, 113–122.
Serrano-Cinca, C., Gutierrez-Nieto, B., & Lopez-Palacios, L. (2015). Determinants of default in P2P lending. PLoS ONE, 10(10), 1–22.
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Railiene, G. (2018). Comparison of Borrower Default Factors in Online Lending. In: Bilgin, M., Danis, H., Demir, E., Can, U. (eds) Consumer Behavior, Organizational Strategy and Financial Economics. Eurasian Studies in Business and Economics, vol 9. Springer, Cham. https://doi.org/10.1007/978-3-319-76288-3_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-76288-3_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-76287-6
Online ISBN: 978-3-319-76288-3
eBook Packages: Business and ManagementBusiness and Management (R0)