In practice, as a well-known statistical method, the logistic regression model is used to
evaluate the credit-worthiness of borrowers due to its simplicity and transparency in …

The need for controlling and effectively managing credit risk has led financial institutions to
excel in improving techniques designed for this purpose, resulting in the development of …

Credit risk assessment plays a key role for correctly supporting financial institutes in defining
their bank policies and commercial strategies. Over the last decade, the emerging of social …

Many years have passed since Baesens et al. published their benchmarking study of
classification algorithms in credit scoring [Baesens, B., Van Gestel, T., Viaene, S …

In the last years, the application of artificial intelligence methods on credit risk assessment
has meant an improvement over classic methods. Small improvements in the systems about …

Widespread adoption of automated decision making by artificial intelligence (AI) is
witnessed due to specular advances in computation power and improvements in …

For the sake of credit risk assessment, credit scoring has become a critical tool to
discriminate “bad” applicants from “good” applicants for financial institutions. Accordingly, a …

Abstract Multi-Layer Perceptron (MLP) neural networks are widely used in automatic credit
scoring systems with high accuracy and efficiency. This paper presents a higher accuracy …

Data mining techniques have numerous applications in credit scoring of customers in the
banking field. One of the most popular data mining techniques is the classification method …

During the last few years there has been marked attention towards hybrid and ensemble
systems development, having proved their ability to be more accurate than single classifier …