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...

Corporate distress models typically only employ the numerical financial variables in the firms' annual reports. We develop a model that employs the unstructured textual data in the reports as well, namely the auditors' reports and managements' statements. Our model consists of a convolutional...

This paper constructs a leading macroeconomic indicator from microeconomic data using recent machine learning techniques. Using tree-based methods, we estimate probabilities of default for publicly traded non-financial firms in the United States. We then use the cross-section of out-of-sample...

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...

Predicting default probabilities is at the core of credit risk management and is becoming more and more important for banks in order to measure their client's degree of risk, and for firms to operate successfully. The SVM with evolutionary feature selection is applied to the CreditReform...

In this paper we study the performance of several machine learning (ML) models for credit default prediction. We do so by using a unique and anonymized database from a major Spanish bank. We compare the statistical performance of a simple and traditionally used model like the Logistic Regression...

Digital technologies produce vast amounts of unstructured data that can be stored and accessed by traditional banks and fintechs. Prior literature on the topic indicates that certain aspects of this unstructured data could be valuable for decisions regarding the acceptance and pricing of credit...

We argue that the true transition-to-default dynamic in banks' credit portfolios can only be fully described with a multiple-spell discrete-time hazard model. This paper develops such a model for default prediction. The model permits the use of all data available to the bank or to the bank...

In this paper we use a reduced form model for the analysis of Portfolio Credit Risk. For this purpose, we fit a Dynamic Factor model, DF, to a large dataset of default rates proxies and macrovariables for Italy. Multi step ahead density and probability forecasts are obtained by employing both...

In this paper we use a reduced form model for the analysis of Portfolio Credit Risk. For this purpose, we fit a Dynamic Factor model, DF, to a large dataset of default rates proxies and macrovariables for Italy. Multi step ahead density and probability forecasts are obtained by employing both...