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 …

Hyperparameter optimization constitutes a large part of typical modern machine learning
workflows. This arises from the fact that machine learning methods and corresponding …

Financial institutions are increasingly leveraging on advanced technologies, facilitated by
the availability of Machine Learning methods that are being integrated into several …

Our fast-paced digital economy shaped by global competition requires increased data-
driven decision-making based on artificial intelligence (AI) and machine learning (ML). The …

The class imbalance problem is common in the credit scoring domain, as the number of
defaulters is usually much less than the number of non-defaulters. To date, research on …

Federated learning has drawn a lot of interest as a powerful technological solution to the
“credit data silo” problem. The interpretability of federated learning is a crucial issue due to …

Abstract Machine-learning-based systems are now part of a wide array of real-world
applications seamlessly embedded in the social realm. In the wake of this realization, strict …

Can satisfactory explanations for complex machine learning models be achieved in high-risk
automated decision-making? How can such explanations be integrated into a data …

The recent advancements in artificial intelligence (AI) combined with the extensive amount
of data generated by today's clinical systems, has led to the development of imaging AI …

Banks' credit scoring models are required by financial authorities to be explainable. This
paper proposes an explainable artificial intelligence (XAI) model for predicting credit default …