This paper proposes a machine learning approach to estimate physical forward default intensities. Default probabilities are computed using artificial neural networks to estimate the intensities of the inhomogeneous Poisson processes governing default process. The major contribution to previous...

Roll rates and net flow rates can be seen as the evolution of ageing of accounts receivable and Markov chains. They are accepted methodologies to model the behavior of non-performing consumer loans by buckets and to predict losses, but we find that quite often they are wrongly used as...

We develop distress prediction models for non-financial small and medium sized enterprises (SMEs) using a dataset from eight European countries over the period 2000-2009. We examine idiosyncratic and systematic covariates and find that macro conditions and bankruptcy codes add predictive power...

We investigate whether and how business credit information sharing helps to better assess the default risk of private firms. Private firms represent an ideal testing ground because they are smaller, more informationally opaque, riskier, and more dependent on trade credit and bank loans than...

This paper proposes a rating methodology that is based on a non-linear classification method, the support vector machine, and a non-parametric technique for mapping rating scores into probabilities of default. We give an introduction to underlying statistical models and represent the results of...

This study aims to evaluate the techniques used for the validation of default probability (DP) models. By generating simulated stress data, we build ideal conditions to assess the adequacy of the metrics in different stress scenarios. In addition, we empirically analyze the evaluation metrics...