Journal of Risk and Financial Management, Volume 15, Issue 12
2022 December - 75 articles
Cover Story: The objective of this study was to apply explainable artificial intelligence (XAI) techniques to credit scoring in banking, in order to interpret and justify black-box-like artificial intelligence (AI) models’ predictions. Current AI models are often perceived as black boxes, whose output is difficult to interpret. With the implementation of the Basel II agreement and the General Data Protection Regulation, European banks must now abide by strict regulations enforcing a certain level of explainability in all decision-making data-based models. We contribute to the literature by implementing an AI-based credit-scoring model on a real-life dataset from a bank, benchmarking it to the bank’s current logistic regression (LR) model to explain and interpret the results using XAI. View this paper - Issues are regarded as officially published after their release is announced to the table of contents alert mailing list .
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