Peer-to-peer (P2P) lending offers an alternative way to access credit. Unlike established lending institutions with proven credit risk management practices, P2P platforms rely on numerous independent variables to evaluate loan applicants’ creditworthiness. This study aims to estimate default probabilities using a mixture-of-experts neural network in P2P lending. The approach involves coupling unsupervised clustering to capture essential data properties with a classification algorithm based on the mixture-of-experts structure. This classic design enhances model capacity without significant computational overhead. The model was tested using P2P data from Lending Club, comparing it to other methods like Logistic Regression, AdaBoost, Gradient Boosting, Decision Tree, Support Vector Machine, and Random Forest. The hybrid model demonstrated superior performance, with a Mean Squared Error reduction of at least 25%.
References
[1]
Alpaydin, E. (2004) Introduction to Machine Learning (Adaptive Computation and Machine Learning Series). Vol. 14, The MIT Press, Cambridge.
[2]
Blanco, A., Pino-Mejías, R., Lara, J. and Rayo, S. (2013) Credit Scoring Models for the Microfinance Industry Using Neural Networks: Evidence from Peru. Expert Systems with Applications,40, 356-364. https://doi.org/10.1016/j.eswa.2012.07.051
[3]
Lenz, R. (2017) Peer-to-Peer Lending: Opportunities and Risks. European Journal of Risk Regulation, 7, 688-700. https://doi.org/10.1017/S1867299X00010126
[4]
Ariza-Garzón, M.J., Camacho-Miñano, M.D.M., Segovia-Vargas, M.J. and Arroyo, J. (2021) Risk-Return Modelling in the p2p Lending Market: Trends, Gaps, Recommendations and Future Directions. Electronic Commerce Research and Applications,49, 1-16. https://doi.org/10.1016/j.elerap.2021.101079
[5]
Evans, R., Pfahringer, B. and Holmes, G. (2011) Clustering for Classification. 2011 7th International Conference on Information Technology in Asia: Emerging Convergences and Singularity of Forms-Proceedings of CITA’11, Sarawak, 12-13 July 2011, 1-8. https://doi.org/10.1109/CITA.2011.5998839
[6]
Machado, M.R. and Karray, S. (2022) Assessing Credit Risk of Commercial Customers Using Hybrid Machine Learning Algorithms. Expert Systems with Applications,200, 116889. https://doi.org/10.1016/j.eswa.2022.116889
Albahri, A.S., Alnoor, A., Zaidan, A.A., Albahri, O.S., Hameed, H., Zaidan, B.B., Peh, S.S., Zain, A.B., Siraj, S.B., Masnan, A.H.B. and Yass, A.A. (2022) Hybrid Artificial Neural Network and Structural Equation Modelling Techniques: A Survey. Complex and Intelligent Systems,8, 1781-1801. https://doi.org/10.1007/s40747-021-00503-w
[9]
Baştanlarm Y. and Ozuysal, M. (2014) Introduction to Machine Learning. In: Yousef, M. and Allmer, J., Eds., miRNomics: MicroRNA Biology andComputational Analysis. Methods in Molecular Biology, Vol. 1107, Humana Press, Totowa, NJ, 105-128. https://doi.org/10.1007/978-1-62703-748-8_7
[10]
West, D. (2000) Neural Network Credit Scoring Models. Computers and Operations Research, 27, 1131-1152. https://doi.org/10.1016/S0305-0548(99)00149-5
[11]
Byanjankar, A., Heikkila, M. and Mezei, J. (2015) Predicting Credit Risk in Peer-to-Peer Lending: A Neural Network Approach. Proceedings-2015 IEEE Symposium Series on Computational Intelligence (SSCI 2015), Cape Town, 7-10 December 2015, 719-725. https://doi.org/10.1109/SSCI.2015.109
[12]
Ince, H. and Aktan, B. (2009) A Comparison of Data Mining Techniques for Credit Scoring in Banking: A Managerial Perspective. Journal of Business Economics and Management,10, 233-240. https://doi.org/10.3846/1611-1699.2009.10.233-240
[13]
Wang, Z., Jiang, C., Ding, Y., Lyu, X. andLiu, Y. (2018) A Novel Behavioral Scoring Model for Estimating Probability of Default over Time in Peer-to-Peer Lending. Electronic Commerce Research and Applications,27, 74-82. https://doi.org/10.1016/j.elerap.2017.12.006
[14]
Fernandes, M.A., Patel, P. and Marwala, T. (2015) Automated Detection of Human Users in Twitter. Procedia Computer Science,53, 224-231. https://doi.org/10.1016/j.procs.2015.07.298
[15]
Weinstock, R.L. (2023) Introduction to U.S. Economy: The Business Cycle and Growth-Congressional Research Service.
[16]
Moran, O. and Ido, T. (2021) An Empirical View of Peer-to-Peer (P2P) Lending Platforms. Berkeley Business Law Journal. (Forthcoming)
[17]
Frigui, H. (2008) Clustering: Algorithms and Applications. 2008 First Workshops on Image Processing Theory, Tools and Applications, Sousse, 23-26 November 2008, 1-11. https://doi.org/10.1109/IPTA.2008.4743793
[18]
Yan, X.X. and An, Q.S. (2020) Research on the Factors Affecting the Competitiveness of P2P Online Loan Platform. Advances in Economics, Business and Management Research, 126, 270-276. https://doi.org/10.2991/aebmr.k.200306.047
