Credit risk assessment is a crucial element in credit risk management. With the extensive
research on consumer credit risk assessment in recent decades, the abundance of literature …

Predictive models are increasingly being used to optimize decision-making and minimize
costs. A conventional approach is predict-then-optimize: first, a predictive model is built; …

Credit unions are growing microfinance institutions that base their lending decisions on the
judgment of their credit analysts. Therefore, the purpose of this paper is to design 6 profit …

The problem of calibrating deep neural networks (DNNs) for multi-label learning is
considered. It is well-known that DNNs trained by cross-entropy for single-label or one-hot …

Credit risk prediction should maximize a bank's loan profit. This paper performs modified
profit-based logistic regression (MPLR) by constructing an objective function with the …

Over the years, a plethora of cost-sensitive methods have been proposed for learning on
data when different types of misclassification errors incur different costs. Our contribution is a …

Credit transactions are vital financial activities that yield substantial economic benefits. To
further improve lending decisions, stakeholders require accurate and interpretable credit …

In credit markets, screening algorithms aim to discriminate between good-type and bad-type
borrowers. However, when doing so, they can also discriminate between individuals sharing …

In this study, we present a novel cost-sensitive approach for uplift modeling in the context of
cross-selling and workforce analytics. We leverage referrals from sales agents across …

The effectiveness of machine learning in evaluating the creditworthiness of loan applicants
has been demonstrated for a long time. However, there is concern that the use of automated …