Exploring Explainable AI in the Financial Sector: Perspectives of Banks and Supervisory Authorities

TL;DR

This paper investigates how banks and supervisory authorities perceive explainable AI in finance, highlighting differences in explainability expectations across use cases and proposing clearer standards for AI transparency and accountability.

Contribution

It provides a preliminary analysis of stakeholder perspectives on xAI in finance, emphasizing the need for differentiated explainability standards aligned with legal and regulatory frameworks.

Findings

Disparity exists between authorities and banks on explainability scope.

Different requirements for model versus system explainability.

Clearer standards could improve AI transparency in finance.

Abstract

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.