Peer-to-Peer (P2P) Lending in Europe: Evaluating the Default Risk of Borrowers in the Context of Gender and Education
Keywords:
Peer-to-peer lending; education; gender gap; financial literacy
Abstract
In recent years, the importance of social lending activities and their effects on consumers have been highlighted by the widespread use of peer-to-peer lending platforms and the global race in fintech. Our study focuses on factors that affect the likelihood that European borrowers on peer-to-peer lending platforms, which are currently based in Estonia, Finland, and Spain, will default on their loans. Starting with the publicly accessible Bondora database, we examine the different economic and social characteristics of the borrowers to analyze the factors that contributed to loan default between 2013 and 2021. We use a Logit model to calculate the ex-post probability of default for factors derived from Principal Component Analysis as well as the original variables supplied by the database. The results show how crucially important education is for borrowers in lowering the risk of default, along with loan characteristics like high debt levels, long loan terms, and high interest rates. In addition, gender plays an important role in determining loan default, with a particular focus on women's conditions within the family. Regarding financial inclusion and its social implications, our findings suggest different ways to improve financial literacy and promote peer-to-peer lending. Future research could develop on the findings by applying them to other lending platforms and countries.
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References
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13. Eid, N., Maltby, J., & Talavera, O. (2016). Income rounding and loan performance in the peer-to-peer market. Available at SSRN 2848372. http://dx.doi.org/10.2139/ssrn.284837
14. Emekter, R., Tu, Y., Jirasakuldech, B., & Lu, M. (2015). Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending. Applied Economics, 47(1), 54-70. https://doi.org/10.1080/00036846.2014.962222
15. Gomez, R., and E. Santor. (2003). Do Peer Group Members Outperform Individual Borrowers? A Test of Peer Group Lending Using Canadian Micro-Credit Data. General Information.
16. Guo, Y., Zhou, W., Luo, C., Liu, C., & Xiong, H. (2016). Instance-based credit risk assessment for investment decisions in P2P lending. European Journal of Operational Research, 249(2), 417-426. https://doi.org/10.1016/j.ejor.2015.05.050
17. Huston, S., J., (2010). Measuring Financial Literacy, The Journal of Consumer Affairs, Vol. 44, No. 2, 2010 ISSN 0022-0078
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19. Iyer, R., Khwaja, A. I., Luttmer, E. F., & Shue, K. (2016). Screening peers softly: Inferring the quality of small borrowers. Management Science, 62(6), 1554-1577. https://doi.org/10.1287/mnsc.2015.2181
20. Jiang, C., Wang, Z., Wang, R., & Ding, Y. (2018). Loan default prediction by combining soft information extracted from descriptive text in online peer-to-peer lending. Annals of Operations Research, 266(1), 511-529. https://doi.org/10.1007/s10479-017-2668-z
21. Jin, G. Z. and Freedman, S. (2014). The Information Value of Online Social Networks: Lessons from Peer-to-Peer Lending. NBER Working Papers. https://doi.org/10.1016/j.ijindorg.2016.09.002
22. Kgoroeadira, R., Burke, A., & van Stel, A. (2019). Small business online loan crowdfunding: who gets funded and what determines the rate of interest?. Small Business Economics, 52(1), 67-87. https://doi.org/10.1007/s11187-017-9986-z
23. Klafft, M. (2008). Online peer-to-peer lending: a lenders' perspective. In Proceedings of the international conference on E-learning, E-business, enterprise information systems, and E-government, EEE (pp. 371-375). http://dx.doi.org/10.2139/ssrn.1352352
24. Lattin, J. M., Carroll, D. J. & Green, P. E. (2003). Analyzing Multiveriate data, Thomson Brooks/Cole
25. Lee, J. Y. (2020). Prediction of Default Risk in Peer-to-Peer Lending Using Structured and Unstructured Data. Journal of Financial Counseling and Planning. DOI:10.1891/JFCP-18-00073
26. Lee, J. and Kim, K. T. (2017). The Increase in Payday Loans and Damaged Credit after the Great Recession , Journal of Family and Economic Issues, June 2018, v. 39, iss. 2, pp. 360-69. https://doi.org/10.1007/s10834-017-9557-0
27. Lin, X., Li, X., & Zheng, Z. (2017). Evaluating borrower’s default risk in peer-to-peer lending: evidence from a lending platform in China. Applied Economics, 49(35), 3538- https://doi.org/10.1080/00036846.2016.1262526
28. Lyócsa, Š., Vašaničová, P., Hadji Misheva, B., & Vateha, M. D. (2022). Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets. Financial Innovation, 8(1), 1-21. https://doi.org/10.1186/s40854-022-00338-
29. Ma, H. Z., & Wang, X. R. (2016). Influencing factor analysis of credit risk in P2P lending based on interpretative structural modeling. Journal of Discrete Mathematical Sciences and Cryptography, 19(3), 777-786. https://doi.org/10.1080/09720529.2016.1178935
30. Milne, A., & Parboteeah, P. (2016). The business models and economics of peer-to-peer lending. http://dx.doi.org/10.2139/ssrn.2763682
31. Nigmonov, A., Shams, S., & Alam, K. (2022). Macroeconomic determinants of loan defaults: evidence from the US peer-to-peer lending market. Research in International Business and Finance, 59, 101516. https://doi.org/10.1016/j.ribaf.2021.101516
32. Omarini, A. E. (2018). Peer-to-peer lending: business model analysis and the platform dilemma. International Journal of Finance, Economics and Trade
33. Pengnate, S., Riggins, F.J. (2020). The role of emotion in P2P microfinance funding: A sentiment analysis approach, International Journal of Information Management Volume 54, October 2020, 102138
34. Polena, M., & Regner, T. (2018). Determinants of borrowers’ default in P2P lending under consideration of the loan risk class. Games, 9(4), 82. https://doi.org/10.3390/g9040082
35. Ravina, E., Gabriel, S. P., Galak, J., Gokli, A., Munro, A., Patel, H., & Qian, D. (2008). Love & loans: the effect of beauty and personal characteristics in credit markets,’SSRN Working Paper 1101647. http://dx.doi.org/10.2139/ssrn.1107307
36. Santoso, W., Trinugroho, I., & Risfandy, T. (2020). What determine loan rate and default status in financial technology online direct lending? Evidence from Indonesia. Emerging Markets Finance and Trade, 56(2), 351-369. https://doi.org/10.1080/1540496X.2019.1605595
37. Serrano-Cinca, C., Gutiérrez-Nieto, B., & López-Palacios, L. (2015). Determinants of default in P2P lending. PloS one, 10(10), e0139427. https://doi.org/10.1371/journal.pone.0139427
38. Tao, Q., Dong, Y., & Lin, Z. (2017). Who can get money? Evidence from the Chinese peer-to-peer lending platform. Information Systems Frontiers, 19(3), 425-441. https://doi.org/10.1007/s10796-017-9751-5
39. Wang, C., Zhang, W., Zhao, X., & Wang, J. (2019). Soft information in online peer-to-peer lending: Evidence from a leading platform in China. Electronic Commerce Research and Applications, 36, 100873. https://doi.org/10.1016/j.elerap.2019.100873
40. Yan, J., Yu, W., & Zhao, J. L. (2015). How signaling and search costs affect information asymmetry in P2P lending: the economics of big data. Financial Innovation, 1(1), 1-11. https://doi.org/10.1186/s40854-015-0018-1
41. Zhou, L., Fujita, H., Ding, H., & Ma, R. (2021). Credit risk modeling on data with two timestamps in peer-to-peer lending by gradient boosting. Applied Soft Computing, 110, 107672. https://doi.org/10.1016/j.asoc.2021.107672
42. Zou, Z., Chen, H. & Zheng, X. (2017). “A Study of Non-performing Loan Behaviour in P2P Lending under Asymmetric Information”. Transformations in Business and Economics, 2017, v. 16, iss. 3, pp. 490-504
2. Atkinson, A. and F. Messy (2012). Measuring Financial Literacy: Results of the OECD / International Network on Financial Education (INFE) Pilot Study, OECD Working Papers on Finance, Insurance and Private Pensions, No. 15, OECD Publishing, Paris, https://doi.org/10.1787/5k9csfs90fr4-en.
3. Bachmann, A., Becker, A., Buerckner, D., Hilker, M., Kock, F., Lehmann, M., & Funk, B. (2011). Online peer-to-peer lending-a literature review. Journal of Internet Banking and Commerce, 16(2), 1.
4. Barasinska, N., & Schäfer, D. (2014). Is crowdfunding different? Evidence on the relation between gender and funding success from a German peer-to-peer lending platform. German Economic Review, 15(4), 436-452. https://doi.org/10.1111/geer.12052
5. Berger, S. C., and Gleisner, F. (2009). Emergence of financial intermediaries in electronic markets: The case of online P2P lending. Business Research, 2(1), 39-65 https://doi.org/10.1007/BF03343528
6. Carmichael, D. (2014). Modeling default for peer-to-peer loans. Available at SSRN 2529240. http://dx.doi.org/10.2139/ssrn.2529240
7. Chen, H., Chong, T. T. L. & She, Y. (2014). A principal component approach to measuring investor sentiment in China. Quantitative Finance Volume 14, 2014 - Issue 4: Special Issue on Behavioral Finance https://doi.org/10.1080/14697688.2013.869698
8. Chen, D., and C. Han. (2015). A Comparative Study of Online P2P Lending in the USA and China. The Journal of Internet Banking and Commerce 2012: 101-116.
9. Chen, X., Huang, B., & Ye, D. (2020). Gender gap in peer-to-peer lending: Evidence from China. Journal of Banking & Finance, 112, 105633. https://doi.org/10.1016/j.jbankfin.2019.105633
10. Demyanyk, Y., Loutskina, E., & Kolliner, D. (2017). Three myths about peer-to-peer loans. Economic Commentary, 2017, 1-6.
11. De Roure, C., Pelizzon, L., & Thakor, A. (2022). P2P lenders versus banks: Cream skimming or bottom fishing?. The Review of Corporate Finance Studies, 11(2), 213-262. https://doi.org/10.1093/rcfs/cfab026
12. Eckel, C. C. and Füllbrunn, S. C. (2015). Thar she blows? Gender, competetion, and bubbles in experimental asset markets. American Economic Review, 105(2), 906-20. https:// DOI: 10.1257/aer.20130683
13. Eid, N., Maltby, J., & Talavera, O. (2016). Income rounding and loan performance in the peer-to-peer market. Available at SSRN 2848372. http://dx.doi.org/10.2139/ssrn.284837
14. Emekter, R., Tu, Y., Jirasakuldech, B., & Lu, M. (2015). Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending. Applied Economics, 47(1), 54-70. https://doi.org/10.1080/00036846.2014.962222
15. Gomez, R., and E. Santor. (2003). Do Peer Group Members Outperform Individual Borrowers? A Test of Peer Group Lending Using Canadian Micro-Credit Data. General Information.
16. Guo, Y., Zhou, W., Luo, C., Liu, C., & Xiong, H. (2016). Instance-based credit risk assessment for investment decisions in P2P lending. European Journal of Operational Research, 249(2), 417-426. https://doi.org/10.1016/j.ejor.2015.05.050
17. Huston, S., J., (2010). Measuring Financial Literacy, The Journal of Consumer Affairs, Vol. 44, No. 2, 2010 ISSN 0022-0078
18. Hsu J. W. (2016). Aging and Strategic Learning: The Impact of Spousal Incentives on Financial Literacy. The Journal of human resources, 51(4), 1036–1067. https://doi.org/10.3368/jhr.51.4.1014-6712r
19. Iyer, R., Khwaja, A. I., Luttmer, E. F., & Shue, K. (2016). Screening peers softly: Inferring the quality of small borrowers. Management Science, 62(6), 1554-1577. https://doi.org/10.1287/mnsc.2015.2181
20. Jiang, C., Wang, Z., Wang, R., & Ding, Y. (2018). Loan default prediction by combining soft information extracted from descriptive text in online peer-to-peer lending. Annals of Operations Research, 266(1), 511-529. https://doi.org/10.1007/s10479-017-2668-z
21. Jin, G. Z. and Freedman, S. (2014). The Information Value of Online Social Networks: Lessons from Peer-to-Peer Lending. NBER Working Papers. https://doi.org/10.1016/j.ijindorg.2016.09.002
22. Kgoroeadira, R., Burke, A., & van Stel, A. (2019). Small business online loan crowdfunding: who gets funded and what determines the rate of interest?. Small Business Economics, 52(1), 67-87. https://doi.org/10.1007/s11187-017-9986-z
23. Klafft, M. (2008). Online peer-to-peer lending: a lenders' perspective. In Proceedings of the international conference on E-learning, E-business, enterprise information systems, and E-government, EEE (pp. 371-375). http://dx.doi.org/10.2139/ssrn.1352352
24. Lattin, J. M., Carroll, D. J. & Green, P. E. (2003). Analyzing Multiveriate data, Thomson Brooks/Cole
25. Lee, J. Y. (2020). Prediction of Default Risk in Peer-to-Peer Lending Using Structured and Unstructured Data. Journal of Financial Counseling and Planning. DOI:10.1891/JFCP-18-00073
26. Lee, J. and Kim, K. T. (2017). The Increase in Payday Loans and Damaged Credit after the Great Recession , Journal of Family and Economic Issues, June 2018, v. 39, iss. 2, pp. 360-69. https://doi.org/10.1007/s10834-017-9557-0
27. Lin, X., Li, X., & Zheng, Z. (2017). Evaluating borrower’s default risk in peer-to-peer lending: evidence from a lending platform in China. Applied Economics, 49(35), 3538- https://doi.org/10.1080/00036846.2016.1262526
28. Lyócsa, Š., Vašaničová, P., Hadji Misheva, B., & Vateha, M. D. (2022). Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets. Financial Innovation, 8(1), 1-21. https://doi.org/10.1186/s40854-022-00338-
29. Ma, H. Z., & Wang, X. R. (2016). Influencing factor analysis of credit risk in P2P lending based on interpretative structural modeling. Journal of Discrete Mathematical Sciences and Cryptography, 19(3), 777-786. https://doi.org/10.1080/09720529.2016.1178935
30. Milne, A., & Parboteeah, P. (2016). The business models and economics of peer-to-peer lending. http://dx.doi.org/10.2139/ssrn.2763682
31. Nigmonov, A., Shams, S., & Alam, K. (2022). Macroeconomic determinants of loan defaults: evidence from the US peer-to-peer lending market. Research in International Business and Finance, 59, 101516. https://doi.org/10.1016/j.ribaf.2021.101516
32. Omarini, A. E. (2018). Peer-to-peer lending: business model analysis and the platform dilemma. International Journal of Finance, Economics and Trade
33. Pengnate, S., Riggins, F.J. (2020). The role of emotion in P2P microfinance funding: A sentiment analysis approach, International Journal of Information Management Volume 54, October 2020, 102138
34. Polena, M., & Regner, T. (2018). Determinants of borrowers’ default in P2P lending under consideration of the loan risk class. Games, 9(4), 82. https://doi.org/10.3390/g9040082
35. Ravina, E., Gabriel, S. P., Galak, J., Gokli, A., Munro, A., Patel, H., & Qian, D. (2008). Love & loans: the effect of beauty and personal characteristics in credit markets,’SSRN Working Paper 1101647. http://dx.doi.org/10.2139/ssrn.1107307
36. Santoso, W., Trinugroho, I., & Risfandy, T. (2020). What determine loan rate and default status in financial technology online direct lending? Evidence from Indonesia. Emerging Markets Finance and Trade, 56(2), 351-369. https://doi.org/10.1080/1540496X.2019.1605595
37. Serrano-Cinca, C., Gutiérrez-Nieto, B., & López-Palacios, L. (2015). Determinants of default in P2P lending. PloS one, 10(10), e0139427. https://doi.org/10.1371/journal.pone.0139427
38. Tao, Q., Dong, Y., & Lin, Z. (2017). Who can get money? Evidence from the Chinese peer-to-peer lending platform. Information Systems Frontiers, 19(3), 425-441. https://doi.org/10.1007/s10796-017-9751-5
39. Wang, C., Zhang, W., Zhao, X., & Wang, J. (2019). Soft information in online peer-to-peer lending: Evidence from a leading platform in China. Electronic Commerce Research and Applications, 36, 100873. https://doi.org/10.1016/j.elerap.2019.100873
40. Yan, J., Yu, W., & Zhao, J. L. (2015). How signaling and search costs affect information asymmetry in P2P lending: the economics of big data. Financial Innovation, 1(1), 1-11. https://doi.org/10.1186/s40854-015-0018-1
41. Zhou, L., Fujita, H., Ding, H., & Ma, R. (2021). Credit risk modeling on data with two timestamps in peer-to-peer lending by gradient boosting. Applied Soft Computing, 110, 107672. https://doi.org/10.1016/j.asoc.2021.107672
42. Zou, Z., Chen, H. & Zheng, X. (2017). “A Study of Non-performing Loan Behaviour in P2P Lending under Asymmetric Information”. Transformations in Business and Economics, 2017, v. 16, iss. 3, pp. 490-504
Published
2023-03-30
How to Cite
Aliano, M., Alnabulsi, K., Cestari, G., & Ragni, S. (2023). Peer-to-Peer (P2P) Lending in Europe: Evaluating the Default Risk of Borrowers in the Context of Gender and Education. European Scientific Journal, ESJ, 19(7), 60. https://doi.org/10.19044/esj.2023.v19n7p60
Section
ESJ Social Sciences
Copyright (c) 2023 Mauro Aliano, Khalil Alnabulsi, Greta Cestari, Stefania Ragni
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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