Logistic Regression has been widely used in the financial service industry for credit scoring models. Despite its advantages in easy interpretation and low computing cost, Logistic Regression is under the criticism of failure to model the nonlinear features of the predictors effect on the dependent variable and therefore might lead to unsatisfactory results. Modern statistical techniques such a...

We apply neural networks that have been trained and pruned using augmented discretized input data for credit scoring. Credit scoring datasets normally contain input data attributes that are continuous (e.g. salary) and discrete (e.g. marital status). In order to improve the accuracy of the neural network prediction, we augment the input data by including the discretized values of the continuous...

We propose a two-stage model for dealing with the temporal degradation of credit scoring models. First, we develop a model from a classical framework, with a static supervised learning setting and binary output. Then, we introduce the time-changing economic factors, using a regression between the macroeconomic data and the internal default in the portfolio. In so doing, the specific risk is cap...

We examine three models for sample selection that are relevant for modeling credit scoring by commercial banks. A binary choice model is used to examine the decision of whether or not to extend credit. The selectivity aspect enters because such models are based on samples of individuals to whom credit has already been given. A regression model with sample selection is suggested for predicting e...

In credit scoring, low-default portfolios are those for which very little default history exists. This makes it problematic for financial institutions to estimate a reliable probability of a customer defaulting on a loan. Banking regulation (Basel II Capital Accord), and best practice, however, necessitate an accurate and valid estimate of the probability of default. In this article the suitabi...

Risk assessment is an important topic for financial institution nowadays, especially in the context of loan applications. Some of these institutions have already implemented their own credit scoring mechanisms to evaluate their clients’ risk and decide based in this indicator. In fact, the information gathered by financial institutions constitutes a valuable source of data for the creation of i...

In this paper we design the neural network consumer credit scoring models for financial institutions where data usually used in previous research are not available. We use extensive primarily accounting data set on transactions and account balances of clients available in each financial institution. As many of these numerous variables are correlated and have very questionable information conten...

For creating or adjusting credit scoring rules, usually only the accepted applicant’s data and default information are available. The missing information for the rejected applicants and the sorting mechanism of the preceding scoring can lead to a sample selection bias. In other words, mostly inferior classification results are achieved if these new rules are applied to the whole population of a...