Credit Scoring and Its Applications, Second Edition : Back Matter Page 1 Bibliography [1]
Acharya, VV, Bharath, ST, and Srinivasan, A. (2007) Does industry-wide distress affect …

In this paper we present a novel approach to credit scoring of retail customers in the banking
industry based on deep learning methods. We used RNNs on fine grained transnational …

This research is aimed at the case of credit scoring in risk management and presents a
novel machine learning method to be used for the default prediction of high-risk branches or …

The main aim of this paper is to help bank management in scoring credit card clients using
machine learning by modelling and predicting the consumer behaviour concerning three …

To boost the application of machine learning (ML) techniques for credit scoring models, the
blackbox problem should be addressed. The primary aim of this paper is to propose a …

We investigate the performance of various survival analysis techniques applied to ten actual
credit data sets from Belgian and UK financial institutions. In the comparison we consider …

Credit scoring, which is typically transformed into a classification problem, is a powerful tool
to manage credit risk since it forecasts the probability of default (PD) of a loan application …

We propose a new dynamic modeling framework for credit risk assessment that extends the
prevailing credit scoring models built upon historical data static settings. The driving idea …

The inclusion of time-varying covariates into survival analysis has led to better predictions of
the time to default in behavioural credit scoring models. However, when these time-varying …

Credit scoring model development is very important for the lending decisions of financial
institutions. The creditworthiness of borrowers is evaluated by assessing their hard and soft …