This article deals with the issue of managing bank credit risk using a cost risk model.
Modeling of bank credit risk management was proposed based on neural-cell technologies …

A common practice in modern explainable AI is to post-hoc explain black-box machine
learning (ML) predictors–such as neural networks–by extracting symbolic knowledge out of …

Abstract Knowledge-extraction methods are applied to ML-based predictors to attain
explainable representations of their operation when the lack of interpretable results …

Abstract Machine learning black boxes, as deep neural networks, are often hard to explain
because their predictions depend on complicated relationships involving a huge amount of …

Procedures aimed at explaining outcomes and behaviour of opaque predictors are
becoming more and more essential as machine learning (ML) black-box (BB) models …

A common practice in modern explainable AI is to post-hoc explain black-box machine
learning (ML) predictors–such as neural networks–by extracting symbolic knowledge out of …

In many studies, the relationship between development of financial markets and economic
growth has been proved. Credit risk is one of problems which banks are faced with it while …

Modern distributed systems require communicating agents to agree on a shared, formal
semantics for the data they exchange and operate upon. The Semantic Web offers tools to …

Symbolic knowledge-extraction (SKE) algorithms proposed by the XAI community to obtain
human-intelligible explanations for opaque machine learning predictors are currently being …

Machine learning black boxes, exemplified by deep neural networks, often exhibit
challenges in interpretability due to their reliance on complicated relationships involving …