Using account level credit-card data from six major commercial banks from January 2009 to December 2013, we apply machine-learning techniques to combined consumer-tradeline, credit-bureau, and macroeconomic variables to predict delinquency. In addition to providing accurate measures of loss...

This study analyses credit default risk for firms in the Asian and Pacific region by applying two methodologies: a Support Vector Machine (SVM) and a logistic regression (Logit). Among different financial ratios suggested as predictors of default, leverage ratios and the company size display a...

Predicting default probabilities is important for firms and banks to operate successfully and to estimate their specific risks. There are many reasons to use nonlinear techniques for predicting bankruptcy from financial ratios. Here we propose the so called Support Vector Machine (SVM) to...

This study analyses credit default risk for firms in the Asian and Pacific region by applying two methodologies: a Support Vector Machine (SVM) and a logistic regression (Logit). Among different financial ratios suggested as predictors of default, leverage ratios and the company size display a...

This paper explores the ability of the Machine Learning (ML) techniques to calibrate models that replicate the outputs of the Vasicek credit risk model. This model measures the loss distribution of a portfolio made up of loans that can be exposed to multiple systemic factors and it is widely...

This study analyses credit default risk for firms in the Asian and Pacific region by applying two methodologies: a Support Vector Machine (SVM) and a logistic regression (Logit). Among different financial ratios suggested as predictors of default, leverage ratios and the company size display a...

We are interested in forecasting bankruptcies in a probabilistic way. Specifically, we compare the classification performance of several statistical and machine-learning techniques, namely discriminant analysis (Altman's Z-score), logistic regression, least-squares support vector machines and...

We propose a new nonlinear classification method based on a Bayesian "sum-of-trees" model, the Bayesian Additive Classification Tree (BACT), which extends the Bayesian Additive Regression Tree (BART) method into the classification context. Like BART, the BACT is a Bayesian nonparametric additive...

We are interested in forecasting bankruptcies in a probabilistic way. Specifically, we compare the classification performance of several statistical and machine-learning techniques, namely discriminant analysis (Altman's Z-score), logistic regression, least-squares support vector machines and...

We are interested in forecasting bankruptcies in a probabilistic way. Specifcally, we compare the classifcation performance of several statistical and machine-learning techniques, namely discriminant analysis (Altman's Z-score), logistic regression, least-squares support vector machines and...