Abstract
Peer-to-peer lending is a new lending approach gaining in popularity. These loans can offer high interest rates, but they are also exposed to credit risk. In fact, high default rates and low recovery rates are the norms. Potential investors want to know the expected profit in these loans, which means they need to model both defaults and recoveries. However, real-world data sets are censored in the sense that they have many ongoing loans, where future payments are unknown. This makes predicting the exact profit in recent loans particularly difficult. In this paper, we present a model that works for censored loans based on monthly default and recovery rates. We use the Bondora data set, which has a large amount of censored and defaulted loans. We show that loan characteristics predicting lower defaults and higher recoveries are usually, but not always, similar. Our predictions have some correlation with the platform’s model, but they are substantially different. Using a more accurate model, it is possible to select loans that are expected to be more profitable. Our model is unbiased, with a relatively low prediction error. Experiments in selecting portfolios of loans with lower or higher Loss Given Default (LGD) demonstrate that our model is useful, whereas predictions based on the platform’s model or credit ratings are not better than random.
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References
Wang, Z., Jiang, C., Ding, Y., Lyu, X. And Liu, Y.: A novel behavioral scoring model for estimating probability of default over time in peer-to-peer lending. Electron. Commer. Res. App. 27, 74–82 (2018)
Klafft, M.: Online peer-to-peer lending: a lenders’ perspective. In: Proceedings of the International Conference on ELearning, E-Business, Enterprise Information Systems, and EGovernment, pp. 371–375. EEE (2008)
Serrano-Cinca, C., Gutierrez-Nieto, B., López-Palacios, L.: Determinants of default in P2P lending. PLoS One 10(10), e0139427 (2015)
Emekter, R., Tu, Y., Jirasakuldech, B., Lu, M.: Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending. Appl. Econ. 47(1), 54–70 (2015)
Lin, X., Li, X., Zheng, Z.: Evaluating borrower’s default risk in peer-to-peer lending: evidence from a lending platform in China. Appl. Econ. 49(35), 3538–3545 (2017)
Malekipirbazari, M., Aksakalli, V.: Risk assessment in social lending via random forests. Expert Syst. Appl. 42(10), 4621–4631 (2015)
Byanjankar, A., Heikkilä, M., Mezei, J.: Predicting credit risk levels in peer-to-peer lending: a neural network approach. In: IEEE Symposium Series on Computational Intelligence, SSCI, pp. 719–725. Cape Town (2015)
Xia, Y., Liu, C., Liu, N.: Cost-sensitive boosted tree for loan evaluation in peer-to-peer lending. Electron. Commer. Res. Appl. 24, 30–49 (2017)
Đurović, A.: Estimating probability of default on peer to peer market-survival analysis approach. J. Central Banking Theor. Pract. 6(2), 149–167 (2017)
Serrano-Cinca, C., Gutiérrez-Nieto, B.: The use of profit scoring as an alternative to credit scoring systems in peer-to-peer (P2P) lending. Decis. Support Syst. 89, 113122 (2016)
Byanjankar, A., Viljanen, M.: Predicting expected profit in ongoing peer-to-peer loans with survival analysis-based profit scoring. In: Intelligent Decision Technologies. Smart Innovation, Systems and Technologies, vol. 142. Springer, Singapore (2019)
Mild, A., Waitz, M., Wöckl, J.: How long can you go?—Overcoming the inability of lenders to set proper interest rates on unsecured peer-to-peer lending markets. J. Bus. Res. 68(6), 1291–1305 (2015)
Zhou, G., Zhang, Y., Luo, S.: P2P network lending, loss given default and credit risks. Sustainability 10(4) (2018)
Papoušková, M., Hajek, P.: Modelling loss given default in peer-to-peer lending using random forests.InL Intelligent Decision Technologies. Smart Innovation, Systems and Technologies, vol. 142. Springer, Singapore (2019)
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Viljanen, M., Byanjankar, A., Pahikkala, T. (2020). Predicting Profitability of Peer-to-Peer Loans with Recovery Models for Censored Data. In: Czarnowski, I., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies. IDT 2020. Smart Innovation, Systems and Technologies, vol 193. Springer, Singapore. https://doi.org/10.1007/978-981-15-5925-9_2
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DOI: https://doi.org/10.1007/978-981-15-5925-9_2
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