Exploring Explainable AI in the Financial Sector: Perspectives of Banks and Supervisory Authorities
It addresses transparency and accountability challenges in finance, but is incremental as it focuses on preliminary perspectives without proposing new methods.
This study investigated the perspectives of banks and supervisory authorities on explainable AI (xAI) in the financial sector, finding a disparity in desired explainability scope for use cases like consumer credit and anti-money laundering, and argued for clearer differentiation between technical and regulatory explainability requirements.
Explainable artificial intelligence (xAI) is seen as a solution to making AI systems less of a black box. It is essential to ensure transparency, fairness, and accountability, which are especially paramount in the financial sector. The aim of this study was a preliminary investigation of the perspectives of supervisory authorities and regulated entities regarding the application of xAI in the fi-nancial sector. Three use cases (consumer credit, credit risk, and anti-money laundering) were examined using semi-structured interviews at three banks and two supervisory authorities in the Netherlands. We found that for the investigated use cases a disparity exists between supervisory authorities and banks regarding the desired scope of explainability of AI systems. We argue that the financial sector could benefit from clear differentiation between technical AI (model) ex-plainability requirements and explainability requirements of the broader AI system in relation to applicable laws and regulations.