Ethical and Regulatory Challenges of AI Adoption in the Banking Sector: A Global Perspective

Abstract

AI is being rapidly adopted across the banking industry and is expected to drive significant improvements in operational efficiency, customer service, risk assessment and new product offerings. But this shift raises a number of ethical and legal challenges, which are rarely accounted for at a worldwide level. This paper examines some important considerations in the application of AI in banking: algorithmic bias and fairness; transparency and explainability; data privacy and consent; accountability and liability; system risk and stability; cross border regulatory fragmentation; governance of 3rd party AI provider/ vendor. Based on global regulatory canvassings and market summaries (e.g., the Financial Stability Board’s surveil of adoption of AI), and via a comparison of the strategies pursued by leading jurisdictions (e.g., EU’s Artificial Intelligence Act), it shows that although they are leveraged across financial institutions for tasks such as credit underwriting, fraud detection, or customer interface handling, self-learning algorithms do often face lackluster new supervision frameworks, black-box model behavior and inconsistent supervisory expectations. Classifying, evaluating and mitigating risks The analysis reveals ethical (e.g. unfair discrimination, hacking of customer data), social (e.g., impact upon employment) and regulatory (compliance failure, operational hazard) risks are highly interconnected: for example, in some cases, regulatory complexity can slow innovation by up to 35%. In conclusion, this world-wide perspective reveals that adopting AI in a responsible way from the standpoint of bank regulation requires an approach rich in diversity — involving solid governance systems, transparency of stakeholders, ongoing monitoring, cross -border regulatory co-ordination and an ethics by design frame work to see their return from AI without undermining trust, consumer protection or financial stability.