JACIII Vol.25 No.5 pp. 530-538
doi: 10.20965/jaciii.2021.p0530


Evaluating Default Risk and Loan Performance in UK Peer-to-Peer Lending: Evidence from Funding Circle

Boyu Xu, Zhifang Su, and Jan Celler

School of Economics and Finance, Huaqiao University
No.269 Chenghua, North Road, Fengze District, Quanzhou, Fujian 362021, China

Corresponding author

June 12, 2020
March 17, 2021
September 20, 2021
P2P lending, default risk, logistic regression, marginal effect, Cox Proportional Hazard regression

The United Kingdom is the third-largest peer-to-peer (P2P) lending market in the world, which is surpassed only by the two dominant forces in P2P investing, China and the United States of America. As an innovative financial market in the UK, P2P lending brings not only many opportunities but also many risks, especially the loan default risk. In this context, this paper uses binary logistic regression and survival analysis to evaluate default risk and loan performance in UK P2P lending. The empirical results indicate that credit group, loan purpose for capital needs, sector type, loan amount, interest rate, loan term, and the age of the company all have a significant impact on the probability of loan default. Among them, the interest rate, loan term, and loan purpose for capital needs are the three most important determinants of the probability of loan defaults and survival time of loans.

Cite this article as:
Boyu Xu, Zhifang Su, and Jan Celler, “Evaluating Default Risk and Loan Performance in UK Peer-to-Peer Lending: Evidence from Funding Circle,” J. Adv. Comput. Intell. Intell. Inform., Vol.25, No.5, pp. 530-538, 2021.
Data files:
  1. [1] M. Rossi, “The New Ways to Raise Capital: An Exploratory Study of Crowdfunding,” Int. J. of Financial Research, Vol.5, No.2, pp. 8-18, 2014.
  2. [2] Financial Conduct Authority, “Disruptive innovation in financial markets,” 2015, [accessed July 6, 2020]
  3. [3] V. Bavoso, “The promise and perils of alternative market-based finance: the case of P2P lending in the UK,” J. of Banking Regulation, Vol.21, No.4, pp. 395-409, 2020.
  4. [4] T. Ziegler and R. Shneor, “Lending Crowdfunding: Principles and Market Development,” R. Shneor, L. Zhao, and B. Flåten (Eds.), “Advances in Crowdfunding,” pp. 63-92, Palgrave Macmillan, 2020.
  5. [5] M. Lin, N. R. Prabhala, and S. Viswanathan, “Judging Borrowers by the Company They Keep: Friendship Networks and Information Asymmetry in Online Peer-to-Peer Lending,” Management Science, Vol.59, No.1, pp. 17-35, 2013.
  6. [6] A. Sannajust, F. Roux, and A. Chaibi, “Crowdfunding in France: A New Revolution?,” J. of Applied Business Research (JABR), Vol.30, No. 6, pp. 1919-1928, 2014.
  7. [7] A. de Janvry, C. Mclntosh, and E. Sadoulet, “The supply- and demand-side impacts of credit market information,” J. of Development Economics, Vol.93, No.2, pp. 173-188, 2010.
  8. [8] O. Havrylchyk and M. Verdier, “The Financial Intermediation Role of the P2P Lending Platforms,” Comparative Economic Studies, Vol.60, pp. 115-130, 2018.
  9. [9] S. Chishti, “How Peer to Peer Lending and Crowdfunding Drive the FinTech Revolution in the UK,” P. Tasca, T. Aste, L. Pelizzon, and N. Perony (Eds.), “Banking Beyond Banks and Money,” pp. 55-68, Springer, 2016.
  10. [10] T. Meyer, “Online P2P lending nibbles at banks’ loan business,” Deutsche Bank Research, Vol.2, No.1, pp. 39-65, 2007.
  11. [11] A. Sufi, “Information asymmetry and financing arrangements: Evidence from syndicated loans,” The J. of Finance, Vol.62, No.2, pp. 629-668, 2007.
  12. [12] M. Klafft, “Peer to peer lending: auctioning microcredits over the internet,” Proc. of the Int. Conf. on Information Systems, Technology and Management, 2008.
  13. [13] G. A. Akerlof, “The Market for “Lemons”: Quality Uncertainty and The Market Mechanism,” P. Diamond and M. Rothschild (Eds.), “Uncertainty in Economics,” pp. 235+237-251, Academic Press, 1978.
  14. [14] J. E. Stiglitz and A. Weiss, “Credit rationing in markets with imperfect information,” The American Economic Review, Vol.71, No.3, pp. 393-410, 1981.
  15. [15] H. Yum, B. Lee, and M. Chae, “From the wisdom of crowds to my own judgment in microfinance through online peer-to-peer lending platforms,” Electronic Commerce Research and Applications, Vol.11, No.5, pp. 469-483, 2012.
  16. [16] M. Herzenstein, S. Sonenshein, and U. M. Dholakia, “Tell me a good story and I may lend you money: The role of narratives in peer-to-peer lending decisions,” J. of Marketing Research, Vol.48, No.SPL, pp. S138-S149, 2011.
  17. [17] S. Ceyhan, X. Shi, and J. Leskovec, “Dynamics of bidding in a P2P lending service: effects of herding and predicting loan success,” Proc. of the 20th Int. Conf. on World Wide Web (WWW2011), pp. 547-556, 2011.
  18. [18] B. Luo and Z. Lin, “A decision tree model for herd behavior and empirical evidence from the online P2P lending market,” Information Systems and e-Business Management, Vol.11, No.1, pp. 141-160, 2013.
  19. [19] J. Michels, “Do Unverifiable Disclosures Matter? Evidence from Peer-to-Peer Lending,” The Accounting Review, Vol.87, No.4, pp. 1385-1413, 2012.
  20. [20] L. Liao and W. Q. Zhang, “Empirical research on P2P network lending: a literature review,” J. of Tsinghua University, Philosophy and Social Sciences, Vol.32, No.2, pp. 186-196, 2017 (in Chinese).
  21. [21] X. Zhang and J. Y. Hu, “Geographical Distance, Information Asymmetry, and Borrower’s Default Risk,” J. of Shandong University, Philosophy and Social Sciences, No.1, pp. 143-153, 2020 (in Chinese).
  22. [22] M. Herzenstein, R. L. Andrews, U. M. Dholakia, and E. Lyandres, “The democratization of personal consumer loans? Determinants of success in online peer-to-peer lending communities,” Boston University School of Management Research Paper, Vol.14, No.6, pp. 1-36, 2008.
  23. [23] J. M. Liberti and M. A. Petersen, “Information: Hard and soft,” The Review of Corporate Finance Studies, Vol.8, No.1, pp. 1-41, 2019.
  24. [24] M. Lin, “Peer-to-peer lending: An empirical study,” Proc. of the 15th Americas Conf. on Information Systems (AMCIS 2009), pp. 132-138, 2009.
  25. [25] R. Emekter, Y. Tu, B. Jirasakuldech, and M. Lu, “Evaluating Credit Risk and Loan Performance in Online Peer-to-Peer (P2P) Lending,” Applied Economics, Vol.47, No.1, pp. 54-70, 2015.
  26. [26] C. Serrano-Cinca, B. Gutiérrez-Nieto, and L. López-Palacios, “Determinants of Default in P2P Lending,” Plos One, Vol.10, No.10, pp. 1-22, 2015.
  27. [27] A. Nigmonov, S. Shams, and K. Alam, “Macroeconomic Determinants of Loan Delinquencies: Evidence from US Peer-to-Peer Lending Market,” 2020, [accessed July 6, 2020]
  28. [28] D. Carmichael, “Modeling Default for Peer-to-Peer Loans,” 2014, [accessed July 6, 2020]
  29. [29] A. Durović, “Estimating probability of default on peer to peer market – Survival analysis approach,” J. of Central Banking Theory and Practice, Vol.6, No.2, pp. 149-167, 2017.
  30. [30] N. Liang, “Impact of Regional Differences on P2P Lending: Evidence from China,” Charles University, 2019, [accessed July 6, 2020]
  31. [31] C. Jiang, Z. Wang, R. Wang, and Y. Ding, “Loan default prediction by combining soft information extracted from descriptive text in online peer-to-peer lending,” Annals of Operations Research, Vol.266, No.1, pp. 511-529, 2018.
  32. [32] J. R. Yao, J. R. Chen, J. Wei, Y. G. Chen, and S. Q. Yang, “The relationship between soft information in loan titles and online peer-to-peer lending: evidence from RenRenDai platform,” Electronic Commerce Research, Vol.19, No.1, pp. 111-129, 2019.
  33. [33] Center For Analytical Finance, “Lemon or cherry? The value of texts in debt crowdfunding,” 2015, [accessed July 6, 2020]
  34. [34] Y. Su and C. L. Lin, “An Empirical Study on the Influencing Factors of P2P Online Borrowers’ Default Behavior,” J. of Financial Development Research, No.1, pp. 70-76, 2017 (in Chinese).
  35. [35] S. Freedman and G. Z. Jin, “The information value of online social networks: lessons from peer-to-peer lending,” Int. J. of Industrial Organization, Vol.51, No.C, pp. 185-222, 2017.
  36. [36] M. Lin and S. Viswanathan, “Home bias in online investments: An empirical study of an online crowdfunding market,” Management Science, Vol.62, No.5, pp. 1393-1414, 2016.
  37. [37] O. Gajda, “Review of Crowdfunding Regulation: Interpretations of Existing Regulation Concerning Crowdfunding in Europe, North America and Israel,” European Crowdfunding Network AISBL, 2017.
  38. [38] D. Bholat and U. Atz, “Peer-to-peer lending and financial innovation in the United Kingdom,” 2016, [accessed July 6, 2020]
  39. [39] L. Gibilaro and G. Mattarocci, “Peer-to-peer lending and real estate mortgages: evidence from United Kingdom,” J. of European Real Estate Research, Vol.11, No.3, pp. 319-334, 2018.
  40. [40] L. Lu, “Solving the SME Financing Puzzle in the UK: Has On-line P2P Lending Got the Midas Touch?,” J. of Int. Banking Law and Regulation, Vol.33, No.12, pp. 449-460, 2018.
  41. [41] Z. M. Qian, H. B. Li, and Y. P. Yu, “A Study of Default Risks in Personal Housing Mortgage Loan of China’s Consumer Finance,” Economic Research J., Vol.45, No.1, pp. 143-152, 2010 (in Chinese).
  42. [42] L. J. Zhang and K. Zhao, “Research on P2P personal credit evaluation model based on BP neural network,” Electronic Technology and Software Engineering, No.12, pp. 9-10, 2015 (in Chinese).
  43. [43] Y. Tu and X. Y. Wang, “P2P lending default risk warning based on machine learning: evidence from PPDAI.COM,” J. of Statistics and Information, Vol.33, No.6, pp. 69-76, 2018 (in Chinese).
  44. [44] C. B. Wu, Y. Yu, and S. Q. Wu, “Method for measuring loan default probability: default ratio model,” Statistics and Decision, No.6, pp. 15-19, 2010 (in Chinese).
  45. [45] P. F. Bai, Q. Q. Duan, and J. L. Li, “The Credit Risk Measurement of Individual Housing Loan Based on the Method of Survival Analysis,” Economic Research J., Vol.14, No.4, pp. 80-83, 2012 (in Chinese).

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Oct. 22, 2021