International Journal of Science, Engineering and TechnologyAuthors: Anamika, Amit Butail
Abstract: The banking sector’s embrace of Generative Artificial Intelligence (GenAI) and Explainable Artificial Intelligence (XAI) is likely to change how financial services firms manage risk, interpret the impact of regulation on their business, and make decisions. Although there is clear commercial interest in both technologies, academic literature has paid comparatively little attention to providing a factual account of their capabilities and limitations. This paper aims to address that gap. Using a systematic review of the literature, practitioner reports, and regulatory guidance published between 2020 and 2025, we identify existing gaps in GenAI technology as applied to credit risk, market risk, operational risk, and anti-money laundering. We also identify existing gaps in XAI techniques, ranging from SHAP-based feature attribution to counterfactual explanation. GenAI technologies present a low-to-moderate risk of hallucination, a lack of transparency in training data, and limited explainability. XAI techniques help but do not fully resolve the explainability problem, particularly in high-frequency trading environments and complex, multi-agent regulatory contexts. We conclude by proposing a pragmatic research agenda for banking technology, risk, and regulatory professionals.
DOI: http://doi.org/10.5281/zenodo.20717108
