The governance challenge behind AI adoption in banking

By Martin Tombs, Field CTO EMEA at Qlik

Artificial intelligence (AI) is becoming an increasingly important part of how financial institutions operate, supporting activities ranging from fraud prevention and risk management to customer service and operational efficiency. As its use expands, however, so too does scrutiny of how the technology is deployed and overseen.

Recent warnings from organisations, including the IMF and Bank of England, have highlighted concerns with AI usage, ranging from cyber threats and systemic vulnerabilities to shortcomings in governance and accountability. As AI becomes more embedded in business processes, firms are being challenged to show that they have the right controls, oversight mechanisms and decision-making frameworks in place to support its use.

Historically, financial services firms have approached emerging technologies with caution, reflecting the highly regulated nature of the sector. AI introduces a new layer of complexity, requiring organisations to oversee systems that can produce unexpected outputs and support decisions that are not always straightforward to interpret or explain.

The growing focus on AI oversight

The importance of accountability is becoming increasingly visible across the sector. Moves such as HSBC appointing its first Chief AI Officer reflect a broader recognition that oversight can no longer sit across disconnected teams or experimental projects. Meanwhile, many institutions, including Barclays and Lloyds Banking Group, have recently joined the Financial Conduct Authority’s initiative to test AI in real-world conditions under strict controls, while the Bank of England has outlined plans to assess potential risks to financial stability through scenario analysis and simulations.

For finance firms, these developments are likely to increase expectations around how AI systems are monitored, tested and governed internally. Organisations will need clearer oversight of third-party AI providers, stronger documentation around how AI models make decisions, and more robust processes for identifying and escalating risks.

Where AI governance efforts fall short 

Despite growing regulatory scrutiny, financial institutions still face significant barriers to implementing stronger AI governance, particularly around fragmented data. Many firms still operate across disconnected systems, making it difficult to create a consistent view across risk, compliance, operations and customer activity.

This becomes more challenging as AI is introduced. Models depend on large volumes of data flowing across multiple systems, but when those systems are siloed, it becomes harder to trace how information is used or how decisions are made. Without clear data lineage, organisations may struggle to validate AI decisions under regulatory scrutiny.

Data quality is becoming just as important as data access. Even advanced AI models can produce unreliable results if they are trained on incomplete, outdated or poorly governed information. For financial institutions operating across complex legacy systems, maintaining accurate, trusted and consistently managed data at scale will be critical as AI adoption accelerates.

Laying the groundwork for scalable adoption

For many finance companies, the next step is transforming these fragmented datasets into stronger data foundations that support AI at scale. This means creating connected, well-governed data environments where information can move consistently across systems, data quality is maintained more effectively, and accountability is embedded into day-to-day operations rather than treated as a standalone compliance exercise.

This joined-up view is particularly valuable across the customer journey. When someone opens a bank account, they move through several stages including identity verification, onboarding, digital registration and their first transactions. Banks need to see that journey as a whole rather than as disconnected steps. With that visibility, teams can investigate issues more quickly, improve services and track results in real time.

Creating organisation-wide ownership of AI outcomes

Building more connected data environments requires a coordinated approach to accountability across institutions, with responsibility formalised rather than sitting in isolation with individual teams. As more firms appoint Chief AI Officers, close collaboration with Chief Data Officers will become increasingly important to ensure AI governance is built on strong data quality, clear ownership and consistent standards across the organisation. In regulated firms, technology teams, data teams, AI specialists, and business stakeholders all share an obligation to understand the importance of data quality and the consequences it has on decision-making.

This more collaborative approach can also improve how teams operate, ensuring insights are not limited to technical functions alone. Strong governance depends as much on operational visibility and human oversight as it does on the models themselves.

Turning AI ambition into operational reality

For many financial institutions, the challenge now is no longer identifying where AI can be applied, but creating the conditions for it to be used effectively and responsibly. Strong data foundations, clear accountability and greater operational visibility will play an increasingly important role in helping organisations understand how AI systems are performing and where intervention may be needed.

Those that put these elements in place will be better positioned to harness AI as a strategic advantage, improving decision-making, strengthening resilience and driving more consistent outcomes across the business.