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 …

Banking as a data intensive subject has been progressing continuously under the promoting
influences of the era of big data. Exploring the advanced big data analytic tools like Data …

Black-box risk scoring models permeate our lives, yet are typically proprietary or opaque.
We propose Distill-and-Compare, an approach to audit such models without probing the …

A major requirement for credit scoring models is to provide a maximally accurate risk
prediction. Additionally, regulators demand these models to be transparent and auditable …

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 …

In the recent decades, credit scoring has become a very important analytical resource for
researchers and financial institutions around the world. It helps to boost both profitability and …

Extensive research has been performed by organizations and academics on models for
credit scoring, an important financial management activity. With novel machine learning …

Modelling consumer credit risk is a crucial task for banks and non-bank financial institutions
to support decision-making on granting loans. To model the overall credit risk of a consumer …

Machine learning and artificial intelligence have achieved a human-level performance in
many application domains, including image classification, speech recognition and machine …

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 …