The adoption of artificial intelligence (AI) is moving from isolated pilot projects to an operational reality across financial services. From detecting fraud and monitoring financial crime to improving customer experiences and supporting internal decision-making, AI is now becoming embedded in the day-to-day running of financial institutions.
But as its influence grows, so too does the expectation that organisations can clearly explain how these systems operate, how decisions are governed and who remains accountable for the business decisions that AI makes.
Recent warnings from the IMF and Bank of England reinforce that AI governance is no longer simply a technical challenge that needs to be overcome, but a core objective for the industry.
Oversight is becoming a strategic priority
This growing focus on accountability is increasingly visible across the sector. HSBC appointing its first Chief AI Officer, for example, reflects 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 joined the Financial Conduct Authority’s initiative to test AI in real-world conditions under strict controls, and 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. They also need to demonstrate that this governance is continuous rather than something carried out at deployment alone. AI models evolve, data changes and customer behaviour shifts over time, so effective oversight requires ongoing monitoring and clear audit trails that show decisions remain appropriate as conditions change.
The data challenge behind AI adoption
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. At the same time, identifying which datasets will improve decision-making, rather than adding complexity, remains a challenge. For financial institutions operating across complex legacy systems, maintaining accurate, trusted and consistently managed data at scale will be critical as AI adoption accelerates, particularly across areas such as fraud detection, anti-money laundering and customer risk systems where siloed data can limit a complete and accurate view of risk.
Creating an AI ready data environment
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. Risk teams, compliance specialists and customer service functions all rely on different datasets, but as AI increasingly connects these previously separate activities, establishing a common data foundation allows insights to flow more effectively throughout the organisation.
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.
Embedding accountability across the business
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. Giving colleagues in retail banking, lending and compliance access to timely information enables faster, more informed decisions at every level and helps embed accountability for AI-driven outcomes in day-to-day operations.
Strong governance depends as much on operational visibility and human oversight as it does on the models themselves, and when incorporated correctly should be viewed as an enabler of innovation instead of a barrier to it. With clear ownership and controls in place, organisations are better positioned to experiment with AI confidently, knowing new use cases can be deployed responsibility and scaled more quickly.
The next phase of AI maturity
The conversation around AI in financial services is entering a new phase. Competitive advantage will increasingly come not from adopting AI first, but from adopting it responsibly at scale. That demands connected data, clear ownership, continuous oversight and governance that evolves alongside the technology itself.
Those that invest in these foundations today will be better equipped to scale AI securely, respond to evolving regulatory expectations and unlock greater value from the technology over the long term.
Martin Tombs
Martin Tombs is Vice President, Global Go-to-Market for Analytics and Feld CTO EMEA at Qlik. He is part of the global transformation team, having previously led the global pre-sales enterprise architecture and enablement team.
