By Martin Tombs, Field CTO EMEA at Qlik
News from the IMF and Bank of England about the risks associated with AI have added fresh urgency to an already complex challenge for financial institutions. From cyber risk and operational resilience to governance blind spots, the implications are no longer theoretical. As a result, organisations are facing growing pressure to demonstrate clear accountability for how AI is used, particularly when decisions impact customer outcomes, market activity and compliance decisions.
Financial services have long approached AI with caution because of the regulatory and operational risks involved. But AI is now becoming more deeply embedded across core functions of the sector, supporting everything from fraud detection and customer service to compliance monitoring and internal operations.
The challenge is that governance frameworks supporting these systems were not designed for what AI has become. While traditional frameworks were built around software logic and well-defined data flows, AI, by contrast, introduces systems that adapt, learn and in some cases produce outputs that are difficult to fully anticipate or explain without robust supporting infrastructure.
Governance is shifting from best practice to expectation
What was once considered best practice is quickly becoming a baseline expectation, with regulatory bodies and central banks becoming increasingly vocal about the need for stronger oversight of AI within 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. At the same time, firms including Barclays and Lloyds Banking Group participating in controlled AI testing initiatives highlight a growing appetite to explore innovation within clearly defined guardrails.
Alongside this, the Bank of England has outlined plans to assess potential risks to financial stability through scenario analysis and simulations. Together these developments point to a future where governance is not reactive but embedded into the design and deployment of AI systems from the outset.
The barriers to strong AI governance
Despite growing regulatory scrutiny, financial institutions still face significant barriers to implementing stronger AI governance, particularly around fragmented data. In many firms, critical information remains spread across disconnected systems, limiting visibility across risk, compliance, operations and customer activity.
This fragmentation becomes more problematic as AI scales. Models depend on large volumes of data flowing across multiple systems, but when those systems are siloed, it becomes harder to trace data lineage, validate outputs or confidently explain how decisions have been reached. Without that transparency, regulatory assurance becomes significantly more difficult to achieve. Equally important is the issue of data quality Even advanced AI models can produce unreliable results if they are trained on incomplete, outdated or poorly governed information. At the same time, identifying which datasets will improve decision-making, rather than adding complexity, remains a challenge. For use cases such as fraud detection, financial crime prevention and customer risk scoring, where precision is critical, these weaknesses can directly impact both compliance and customer trust.
Building the foundations for responsible AI
The next step in AI maturity for financial services depends on moving away from fragmented datasets towards connected, well-governed data environments where information can move consistently across systems, data quality is maintained more effectively, and accountability is built into operational processes rather than layered on top as an afterthought.
This becomes especially important when viewed through the customer journey. A single interaction, such as opening a bank account, spans several stages including identity verification, onboarding, digital registration and their first transactions. Treating this journey as a whole rather than as disconnected steps allows teams to investigate issues more quickly, improve services and track results in real time.
Accountability cannot sit in one place
As AI becomes more embedded, governance can no longer sit solely with a single function or role. With more firms appointing 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 must all recognise how data quality influences decision-making and understand the potential consequences of poor data practices. Greater collaboration also improves how teams operate, ensuring insights are not limited to technical functions alone. When teams in retail banking, lending and compliance have access to timely, trusted information, they are better equipped to make informed decisions and take ownership of AI-driven outcomes. Strong governance depends as much on operational visibility and human oversight as it does on the models themselves.
Preparing for AI adoption at scale
The financial services sector is entering a new phase of AI adoption, moving beyond isolated AI pilots towards broader adoption at scale. However, scaling AI must happen in a way that remains controlled and transparent. Organisations that can build the right foundations now will be better place to expand AI use confidently. Those that fail to do so may face fragmented processes, heightened operational risk and increasing regulatory scrutiny. Ultimately, the firms that succeed in the financial sector will be those that combine innovation with accountability, using AI to improve efficiency and decision-making while preserving trust through strong governance and clear human oversight.
