Abstract
The paper reviews the possibilities of using survival analysis tools to configure scoring systems for p2p lending platform. Along with the Cox model, the models of log-logistic regression, accelerated failure time (AFT) model and Weibull regression were considered in this study. To test the stability of the factor influence the models were built when discretizing the observation period (12 months, 24 months and 36 months). The sample consisted of 887,379 observations for the period of 2007–2016. The study examined loans issued for the period of 36 months. Proportional hazard models were also analyzed taking into account the grouping feature of borrowers reditworthiness. The best model describing the state duration before the default was chosen. As a result of the analysis the factors affecting the probability of the borrower default during the considered period of time were revealed. It was determined that the greatest influence on the default risk was exerted by the purpose of loan and the interest rate regardless of the considered dynamics. The borrower’s income also had a significant impact on the default risk.
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Agapitov, A., Lakman, I., Maksimenko, Z., Efimenko, N. (2019). An Approach to Developing a Scoring System for Peer-to-Peer (p2p) Lending Platform. In: Petrucci, A., Racioppi, F., Verde, R. (eds) New Statistical Developments in Data Science. SIS 2017. Springer Proceedings in Mathematics & Statistics, vol 288. Springer, Cham. https://doi.org/10.1007/978-3-030-21158-5_26
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