Automatic credit scoring, a crucial risk management tool for banks and financial institutes, has attracted much attention in the past few decades. As such, various approaches have been developed to accurately efficiently estimate defaults loan applicants seamlessly improve facilitate decision-making lending process. However, imbalanced nature of scoring datasets, as well heterogeneous features ...

Inappropriate management in some fields such as credit allocation has imposed too many losses to financial institutions and even has forced some of them to go bankrupt. Moreover, large volume data sets collected by credit departments has necessitated utilizing highly accurate models with less complexities. Credit scoring models with classification and forecasting customers into two groups good ...

As the credit industry has been growing rapidly, credit scoring models have been widely used by the financial industry during this time to improve cash flow and credit collections. However, a large amount of redundant information and features are involved in the credit dataset, which leads to lower accuracy and higher complexity of the credit scoring model. So, effective feature selection metho...

Credit is a widely used tool to finance personal and corporate projects. The risk of default has motivated lenders to use a credit scoring system, which helps them make more efficient decisions about whom to extend credit. Credit scores serve as a financial user model, and have been traditionally computed from the user’s past financial history. As a result, people without any prior financial hi...

This paper presents a novel model of the lending process that takes into account that loan officers must spend time and effort to originate new loans. Besides generating predictions on loan officers’ compensation and its interaction with the loan review process, the model sheds light on why competition could lead to excessively low lending standards. We also show how more intense competition ma...

This paper proposes a DEA-based approach to credit scoring. Compared with conventional models such as multiple discriminant analysis, logistic regression analysis, and neural networks for business failure prediction, which require extra a priori information, this new approach solely requires ex-post information to calculate credit scores. For the empirical evidence, this methodology was applied...