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 …

As the range of decisions made by Artificial Intelligence (AI) expands, the need for
Explainable AI (XAI) becomes increasingly critical. The reasoning behind the specific …

This article examines the integration of blockchain, eXplainable Artificial Intelligence (XAI),
especially in the context of federated learning, for credit scoring in financial sectors to …

ABSTRACT Many Artificial Intelligence (AI) systems are black boxes, which hinders their
deployment. Explainable AI (XAI) approaches which automatically generate counterfactual …

Explainable Artificial Intelligence (XAI) is a growing area of research that aims to improve the
interpretability of the not-so-informative black-box models. However, it is currently difficult to …

Causal Inference plays an significant role in explaining the decisions taken by statistical
models and artificial intelligence models. Of late, this field started attracting the attention of …

Financial institutions benefit from the advanced predictive performance of machine learning
algorithms in automatic decision-making for credit scoring. However, two main challenges …

We describe the design, the architecture, and the evaluation of the Leasing Score Prediction
(LSP) system-a credit scoring and credit rating system for the leasing sector deployed at the …

The accuracy and understandability of bank failure prediction models are crucial. While
interpretable models like logistic regression are favored for their explainability, complex …

Explainable Artificial Intelligence (XAI) is a cutting-edge AI development motivated by the
need for transparency of black-box models in AI systems. This transparency enhances user …