Explainable AI in banking
Abstract
Artificial intelligence (AI) is becoming more commonplace in the financial sector. Consequently, various legislators and supervisory authorities, including the European Commission and the European Banking Authority, are considering the development of AI-specific policies and/or regulations. Explainability seems to be at the heart of the current debate on responsible AI. When financial firms start to deploy advanced data analytics, such as deep neural networks, in their business processes, the need we feel to understand what is going on ‘under the hood’, naturally, becomes stronger. After all, while techniques such as deep learning are able to achieve extremely high levels of accuracy, they are also at the root of the ‘black box’ problem. While it is easy to agree on the importance of explainable AI, agreement on what explainability entails can be a bit more challenging. And even if we can agree on a definition of explainability, agreement on the right trade-off between explainability and performance is a different matter entirely. The challenge we face, both as regulators and as financial firms, is to understand the risks we are dealing with and to conscientiously assess what level (and what kind) of explainability is warranted in different situations. While this is certainly no easy task, it is part and parcel of what constitutes responsible AI in the financial sector. This paper concludes that trust is crucial when it comes to AI in banking. Complex AI technologies are new, and it will take time for banks supervisors, as well as wider society, to grow comfortable with their application. Banks must ensure this technology is deployed conscientiously across the banking system, which may require certain changes to the regulatory framework.
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Regulators and supervisors require banks to illuminate their activities when using AI, hence requiring the deployment of XAI models in specific application scenarios.
“Regulators and supervisors require banks to illuminate their activities when using AI (Burgt 2020; Weber etal. 2020; Adams and Hagras 2020), hence requiring the deployment of XAI models in specific application scenarios.”
From Applications of Explainable Artificial Intelligence in Finance—a systematic review of Finance, Information Systems, and Computer Science literature (2024) · cites this paper as Burgt 2020
System transparency can improve user-system performance and can play a critical role in avoiding harmful societal risks.
“Researchers have demonstrated that system transparency can improve user-system performance, and can play a critical role in avoiding harmful societal risks (Zhou et al., 2019;Burgt, 2020).”
From Think about the stakeholders first! Toward an algorithmic transparency playbook for regulatory compliance (2023) · cites this paper as Burgt, 2020
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“Researchers have demonstrated that system transparency can improve user-system performance, and can play a critical role in avoiding harmful societal risks (Zhou et al., 2019; Burgt, 2020).”
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“It is also relevant for AI when the basis of decision-making by neural networks is too difficult to understand (Burgt, 2020).”
BackgroundAppukuttan, A., & Reghunath, R. (2025) · Environmental Monitoring and Assessment · doi:10.1007/s10661-025-14779-9 · Cited by 8 · see citing works
“However, many ML models, such as SVM and ANN, suffer from a lack of interpretability—mak-ing their decisions difficult for resource managers to justify (Harle & Wankhade., 2025; Mussa et al., 2024; Rudin., 2019; van der Burgt., 2020).”
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Baltgailis, J., Simakhova, A., & Buka, S. A. (2024) · Baltic Journal of Economic Studies · doi:10.30525/2256-0742/2024-10-3-26-35 · Cited by 7 · see citing works
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Revolutionizing the Financial Landscape
Amarnath, D. D., & Thimothy, S. J. (2024) · Advances in finance, accounting, and economics book series · doi:10.4018/979-8-3693-1503-3.ch003 · Cited by 5 · see citing works
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Credit Risk Analysis using Explainable Artificial Intelligence
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Addae-Mensah, K., & Qu, Y. (2025) · European Journal of Electrical Engineering and Computer Science · doi:10.24018/ejece.2025.9.3.715
“XAI introduces transparency by explaining access control decisions to financial institutions and regulators, allowing them to understand authentication results better [15].”
Cognitive Bias and Trust in Digital Accounting Decisions
Lampropoulos, I. C., Aggelopoulos, E., Paraschi, E. P., Georgopoulos, N., & Καλογερά, Μ. (2026) · FinTech · doi:10.3390/fintech5020049
“[63] emphasizes tailoring the level of explanation, while ref.”
- Authors
- Burgt, J. V. D.
- Affiliations
- De Nederlandsche Bank (NL)
- Corresponding author
- Burgt, Joost Van Der
- Venue
- Journal of digital banking.
- Publisher
- Henry Stewart Publications
- Published
- 2020-03-01
- Volume
- 4
- Issue
- 4
- Pages
- 344
- ISSN
- 2397-060X (ISSN-L)
- 2397-0618
- OpenAlex
- W4400995471
- Cited by
- 19
- References
- 0
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 5 · 2023: 5 · 2022: 2 · 2021: 1 · 2020: 1
- FWCI
- 0.88
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of digital banking. (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Law › Digitalization, Law, and Regulation
- Keywords
- explainable AI, banking sector, responsible AI, AI regulation, black box problem, trust in AI, financial supervision, deep learning, European Banking Authority
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