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A1116
Title: Measuring the default risk of small business loans: Improving credit risk prediction using deep learning Authors:  Elias Tzavalis - Athens University of Economics & Business (Greece)
Aikaterini Cheimarioti - Alpha Bank (Greece)
Yiannis Dendramis - Athens University of Economics and Business (Greece) [presenting]
Abstract: A multilayer artificial neural network (ANN) method, known as deep learning ANN, is suggested to predict the probability of default (PD) within the survival analysis framework. Deep learning ANN structures consider hidden interconnections among the covariates determining the PD, which can lead to prediction gains compared to parametric statistical methods. The application of the ANN method to a large data set of small business loans demonstrates prediction gains for the method relative to the logit and skewed logit models. These gains mainly concern short term prediction horizons. They are more apparent for the type I misclassification error of loan default events, which has important implications for bank loans portfolio management. To identify the effects of covariates on the PD by the ANN structure, the paper proposes a bootstrap sampling method obtaining the distribution of changes of the PD over discrete covariate changes, while controlling for possible interactions among the covariates. We find that the covariates with the most important influence on the PD include the delinquent amount of a loan over its total balance, the payments and the balance of the loan over its instalment, as well as the delinquency buckets of a loan. The duration of a loan is also found to be an important factor of default risk.