Diego S. Rodrigues
ABSTRACT
The Peer-to-Peer (P2P) on-line lending is an emerging lending modality that brings creditors and borrowers closer while enabling a significant reduction of bureaucracy in the lending process. Despite its appealing, the increase in the demand for this lending modality depends on a rigorous settlement of the risk assignment to the potential borrowers. Considering this issue, this article discusses an experimental analysis of classification methods for P2P on-line lending default prediction. The performed experiments were based on the application of the implemented classification algorithms over the data mass formed by borrowers' profiles and loan history records of the P2P Lending Club platform. As the main contribution, the study revealed that it is possible to obtain satisfactory prediction results with a set of attributes smaller than those that were used in studies previously presented in the literature. In addition, it could be verified that, since the algorithms based on decision trees have proved highly effective, the use of these methods is a feasible approach to support the development of lending negotiation tools.
- {n. d.}. FICO® | FICO Decisions. http://www.fico.com/br/. (Accessed on 03/04/2018).Google Scholar
- {n. d.}. Peer to Peer Lending & Alternative Investing | Invest with LendingClub. https://www.lendingclub.com/. (Accessed on 03/03/2018).Google Scholar
- Naomi S Altman. 1992. An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician 46, 3 (1992), 175--185.Google ScholarCross Ref
- A Ashta and D Assadi. 2009. Do social cause and social technology meet? Impact of web 2.0 technologies on peer-to-peer lending transactions in general and microfinance in particular. In First European Research Conference on Microfinance, Brussels. 2--4.Google Scholar
- Sven C Berger and Fabian Gleisner. 2010. Emergence of financial intermediaries in electronic markets: The case of online P2P lending. BuR: Business Research 2, 1 (2010), 39--65.Google ScholarCross Ref
- Leo Breiman. 2001. Random forests. Machine learning 45, 1 (2001), 5--32. Google ScholarDigital Library
- Ajay Byanjankar, Markku Heikkilä, and Jozsef Mezei. 2015. Predicting credit risk in peer-to-peer lending: A neural network approach. In Computational Intelligence, 2015 IEEE Symposium Series on. IEEE, 719--725.Google ScholarCross Ref
- Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer. 2002. SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research 16 (2002), 321--357. Google ScholarCross Ref
- Tianqi Chen and Carlos Guestrin. 2016. XGBoost: Reliable Large-scale Tree Boosting System. arXiv 2016; 1--6. arXiv preprint arXiv:1603.02754 (2016).Google ScholarDigital Library
- Michael Conlin. 1999. Peer group micro-lending programs in Canada and the United States. Journal of Development Economics 60, 1 (oct 1999), 249--269.Google ScholarCross Ref
- Corinna Cortes and Vladimir Vapnik. 1995. Support vector machine. Machine learning 20, 3 (1995), 273--297. Google ScholarDigital Library
- David R Cox. 1958. The regression analysis of binary sequences. Journal of the Royal Statistical Society. Series B (Methodological) (1958), 215--242.Google Scholar
- Riza Emekter, Yanbin Tu, Benjamas Jirasakuldech, and Min Lu. 2015. Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending. Applied Economics 47, 1 (2015), 54--70.Google ScholarCross Ref
- Jerome H Friedman. 2001. Greedy function approximation: a gradient boosting machine. Annals of statistics (2001), 1189--1232.Google Scholar
- David W Hosmer Jr, Stanley Lemeshow, and Rodney X Sturdivant. 2013. Applied logistic regression. Vol. 398. John Wiley & Sons.Google ScholarCross Ref
- Michael K Hulme and Collette Wright. 2006. Internet based social lending: Past, present and future. Social Futures Observatory 115 (2006).Google Scholar
- Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2013. An introduction to statistical learning. Vol. 112. Springer. Google ScholarDigital Library
- David D Lewis. 1998. Naive (Bayes) at forty: The independence assumption in information retrieval. In European conference on machine learning. Springer, 4--15. Google ScholarDigital Library
- Zhiyong Li, Xiao Yao, Qing Wen, and Wei Yang. 2016. Prepayment and Default of Consumer Loans in Online Lending. (2016).Google Scholar
- Milad Malekipirbazari and Vural Aksakalli. 2015. Risk assessment in social lending via random forests. Expert Systems with Applications 42, 10 (2015), 4621--4631. Google ScholarDigital Library
- Nilas Möllenkamp. 2017. Determinants of Loan Performance in P2P Lending. B.S. thesis. University of Twente.Google Scholar
- F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12 (2011), 2825--2830. Google ScholarDigital Library
- Michel Polena and Regner Tobias. 2016. Determinants of borrowers' default in P2P lending under consideration of the loanrisk class. Jena Economic Research Paper (2016), 1--30.Google Scholar
- Carlos Serrano-Cinca, Begoña Gutiérrez-Nieto, and Luz López-Palacios. 2015. Determinants of default in P2P lending. PloS one 10, 10 (2015), e0139427.Google ScholarCross Ref
- Vladimir Vapnik. 2013. The nature of statistical learning theory. Springer science & business media.Google ScholarDigital Library
- Strother H Walker and David B Duncan. 1967. Estimation of the probability of an event as a function of several independent variables. Biometrika 54, 1--2 (1967), 167--179.Google ScholarCross Ref
- Min Xu, Pakorn Watanachaturaporn, Pramod K Varshney, and Manoj K Arora. 2005. Decision tree regression for soft classification of remote sensing data. Remote Sensing of Environment 97, 3 (2005), 322--336.Google ScholarCross Ref
- Yuejin Zhang, Haifeng Li, Mo Hai, Jiaxuan Li, and Aihua Li. 2017. Determinants of loan funded successful in online P2P Lending. Procedia Computer Science 122 (2017), 896--901.Google ScholarCross Ref
Index Terms
- A Comparative Analysis of Loan Requests Classification Algorithms in a Peer-to-Peer Lending Platform
Recommendations
On the effects of misspellings on lender demand in peer-to-peer lending
I analyse borrower submitted free-form descriptions of anonymous peer-to-peer (P2P) loans from the US-based P2P lending website LendingClub.com and find that borrower misspellings predict lower funding rates, and longer times to fund. Prior empirical ...
Who can get money? Evidence from the Chinese peer-to-peer lending platform
This paper explores how borrowers' financial and personal information, loan characteristics and lending models affect peer-to-peer (P2P) loan funding outcomes. Using a large sample of listings from one of the largest Chinese online P2P lending platforms,...
Recommendation Engine for Lower Interest Borrowing on Peer to Peer Lending (P2PL) Platform
WI '19: IEEE/WIC/ACM International Conference on Web IntelligenceOnline Peer to Peer Lending (P2PL) systems connect lenders and borrowers directly, thereby making it convenient to borrow and lend money without intermediaries such as banks. Many recommendation systems have been developed for lenders to achieve higher ...
Comments