Building Trustworthy AI in Banking: Governance, Ethics, and Explainability in Practice

In the banking industry, artificial intelligence (AI) is increasingly being used to improve efficiency and decision-making. Yet, as AI becomes more integral to financial services, the need for trust and accountability grows. This is where governance, ethics, and explainability come into play. They are the backbone of trustworthy AI systems, helping to mitigate risks such as bias, privacy violations, and market instability.

The Role of Governance in AI

Governance is about setting clear rules and standards for AI use. In banking, this means establishing policies that dictate how AI models are developed, tested, and deployed. Effective governance involves oversight mechanisms to catch potential issues early, such as bias in credit assessments or insurance risk analyses. The European Central Bank, for instance, is enhancing its Supervisory Review and Evaluation Process (SREP) to incorporate more qualitative methods, emphasizing compliance and risk management.

In the U.S., state-level regulations are being enacted, addressing issues like algorithmic transparency and consumer protection. Federal agencies also enforce existing laws, such as the Equal Credit Opportunity Act (ECOA) and the Fair Credit Reporting Act (FCRA), to oversee AI applications in finance. These efforts aim to create a framework that balances AI's benefits with the need for accountability.

Ethics in AI Development

Ethics in AI development involves designing systems that are fair, transparent, and respectful of privacy. This means ensuring that AI models do not discriminate against certain groups and that data used is secure and consensual. The EU's AI Act, set to take effect by mid-2025, classifies AI systems by risk, with high-risk applications facing stricter regulations[1]. This approach highlights the importance of considering ethical implications from the outset.

Ethical AI is not just a compliance requirement; it is also a competitive advantage. Banks that demonstrate ethical AI practices build trust with customers and regulators alike, enhancing their reputation and operational efficiency. As AI continues to influence decision-making, ensuring ethical considerations are integrated into AI development will be key to maintaining trust and stability in the financial sector.

The Importance of Explainability

Explainability is crucial for building trust in AI systems. It involves making AI decisions understandable to both humans and machines. This is particularly important in banking, where AI models are used for high-stakes decisions like credit approval or investment advice. If AI models are not explainable, it can lead to mistrust and regulatory scrutiny.

The Monetary Authority of Singapore (MAS) has emphasized the need for model risk management governance and testing in AI solutions. This includes ensuring that AI models are transparent and reliable, with clear documentation and validation processes[2]. Such measures help banks to provide evidence of their AI systems' integrity and reliability, which is essential for maintaining regulatory compliance.

Solving Painpoints with AI Governance

One of the biggest challenges in implementing AI in banking is ensuring compliance with diverse and often complex regulations. This can be a significant pain point for institutions looking to benefit from AI without risking non-compliance.

Banks can address these challenges proactively, by establishing clear governance structures and ethical guidelines, . For example, implementing a comprehensive Gen AI governance program, as suggested by FINRA, can help firms identify low-risk AI use cases and ensure that their AI tools comply with existing regulations[5]. This approach not only mitigates risks but also enhances operational efficiency and customer trust.

Emotional Connection: Trust in AI

Trust is an emotional connection that customers have with banks. When AI systems are transparent and ethical, they foster this trust. Imagine a customer who receives a credit denial without understanding why. Lack of transparency can lead to frustration and mistrust. But when AI decisions are explainable, customers feel more secure in their dealings with banks.

Banks that prioritize ethics and transparency in AI use can build a strong reputation and foster long-term relationships with customers. This is not just about compliance; it's about creating a sense of security and trust that is essential for the financial sector.

The Question of Responsibility

As AI becomes more autonomous, there's a growing question: Who is responsible when AI makes a decision? This is a challenge that banks and regulators must address. Ensuring that AI systems are designed to be accountable and transparent is key to resolving this issue. Banks must be ready to explain AI-driven decisions and ensure that their systems are free from bias and error.

In 2025, banks are expected to spend significantly on AI initiatives, with predictions suggesting an increase from $6 billion in 2024 to $9 billion in 2025. This investment underscores the potential of AI to reshape the banking sector, but it also highlights the need for clear governance and ethical standards to guide these developments.

Ultimately, building trustworthy AI in banking requires a commitment to governance, ethics, and explainability. By focusing on these aspects, banks can create AI systems that are reliable, fair, and transparent—key attributes for instilling trust in both customers and regulators. As AI continues to play a larger role in financial services, the importance of these elements will only grow.