In this guest blog, we’re pleased to welcome insights from David Bull, Senior Consultant at Talan. With over 15 years of experience across Retail, Commercial, and Investment Banking, as well as industries including Transport and Renewable Energy, David has led projects spanning data analytics, regulatory change, and remediation. Drawing on his expertise in data governance and AI, David explores how financial institutions can innovate responsibly under the UK’s evolving regulatory approach, while keeping fairness, accountability, and consumer trust at the centre.
Artificial Intelligence (AI) and data are no longer just buzzwords in financial services, they’re reshaping how firms operate, comply, and engage with customers. From automating regulatory reporting to enhancing fraud detection, AI is unlocking new efficiencies and insights. But with great power comes great responsibility. In the UK, the Financial Conduct Authority (FCA) is taking a principles-based approach to AI regulation, focusing on outcomes like fairness and accountability rather than introducing rigid new rules, as seen in the EU’s AI Act. This flexible stance encourages innovation while keeping consumer protection front and centre.
The UK’s Regulatory Shift
The FCA’s five guiding principles – safety, transparency, fairness, accountability, and contestability – are designed to ensure AI is used responsibly (FCA AI Update). Rather than create new frameworks, the FCA is embedding these principles into existing regimes like the Senior Managers and Certification Regime (SM&CR) and Consumer Duty.
To support innovation, the FCA has launched initiatives such as the AI Sprint, which brought together regulators, technologists, and financial institutions to explore real-world use cases. The upcoming AI Live Testing programme will allow firms to trial AI solutions in a controlled environment, while the Digital Sandbox provides a collaborative space for testing with synthetic data.
Data Governance and Compliance to Support AI Adoption
While AI gets the headlines, it’s data that underpins everything. Poor data quality can undermine even the most sophisticated AI models, leading to biased decisions, regulatory breaches, and reputational damage – imagine an AI model ‘let loose’ on inaccurate or ambiguous data! That’s why robust data governance is essential for compliance, but equally for building trustworthy AI systems.
In one Customer Due Diligence (CDD) project, Talan Data x AI helped a multinational bank reconcile customer identity data across multiple jurisdictions, ensuring compliance with both local and global anti money laundering (AML) requirements. This involved building a unified data model, implementing real-time validation checks, and creating audit trails to support regulatory reporting.
Another common challenge we encounter is conflicting consent data. For instance, a customer might opt out of marketing communications via one channel but remain opted-in on another due to system fragmentation. This inconsistency can result in breaches of ePrivacy regulations, damage customer trust, and violate the FCA’s principles of fairness and transparency. The solution is good data management & governance.
In an ePrivacy focussed project for a large retail bank, we helped the client consolidate over 40 separate data stores into a single unified database containing every customer’s contact preferences and marketing permissions. These permissions can vary for both channel (email, telephone etc) and product (Credit Cards, Current Accounts, Loans etc) and all customer contact should consider the relevant combinations for that activity – a potentially complex decision. Our solution streamlined this process, delivering clear benefits to both customers and the bank. For customers, their preferences are ‘honoured’, only receiving the communication they have specifically signed up for. The bank can confidently develop new marketing campaigns targeting the largest permissible groups of current or potential customers, resulting in improved engagement and higher response rates.
We also help clients build data lineage frameworks which are critical for demonstrating how data flows through systems and how decisions are made. This is particularly important for AI applications, where regulators expect firms to explain how an algorithm arrived at a particular outcome. By embedding traceability and auditability into data pipelines, we enable firms to meet the FCA’s expectations around accountability and contestability.
Ultimately, good data governance isn’t just a compliance exercise – it’s a business enabler. It allows firms to innovate faster (including with AI), serve customers better, and build systems that are fair, transparent, and resilient.
Consumer Outcomes and Ethical AI
AI presents both an opportunity and a threat for fair and transparent Consumer Outcomes. The FCA has emphasised that firms must ensure decisions made by AI are explainable and contestable, particularly where they affect access to financial products. This includes having mechanisms for redress when AI decisions cause harm. It also provides for improved knowledge usage and better communication in firms’ interactions with customers which, with appropriate oversight, can accelerate positive consumer outcomes, as we’re expecting to see in any forthcoming actions related to the Car Finance redress scheme.
Recent joint research by the FCA and Bank of England found that while 75% of UK firms use AI, only 34% fully understand the systems they deploy. This raises concerns about accountability and governance, especially as AI models become more complex and reliant on third-party providers.
To avoid digital exclusion, inclusive design and human oversight are essential. Ethical AI should work for all consumers, not just the digitally fluent, helping build trust and ensure technology serves the public interest.
Talan’s RegTech Innovation Journey
Talan Data x AI has been actively contributing to this evolving landscape. Through our involvement in the Financial Regulation Innovation Lab (FRIL) and Fintech Scotland’s Innovation Call, we have developed a RegTech accelerator that simplifies compliance using natural language processing (NLP) and machine learning (ML).
The accelerator processes complex regulatory documents, extracts key insights, and presents them in intuitive dashboards tailored for compliance teams to take appropriate actions. It’s built with a “human-in-the-loop” design, ensuring decisions made by AI can be reviewed, challenged, and explained, aligning with the FCA’s principles of accountability and contestability.
The Path Forward: Trustworthy AI Through Better Data
The right approach to AI in Financial Services is grounded in robust data governance, aligning closely with UK regulatory priorities. By ensuring data is accurate, representative, and responsibly managed, financial institutions can build AI systems that are fair, transparent, and accountable. As the FCA and ICO continue to stress the importance of data quality and oversight, Talan Data x AI’s focus remains on helping firms innovate responsibly, creating a resilient consumer-centric financial ecosystem that earns trust, delivers value, and adapts to change.
Finance Month at The Data Lab Community
Interested in taking a deeper dive into how data and AI are transforming the financial sector?
Here’s what we’ve got lined up:
- Transforming Financial Services through Scalable AI & Data Innovation –10th September, 17:30-19:30, Bank of Scotland Head Office, Edinburgh
- Using Smart Data for a Real-world View of Economic Wellbeing –23rd September, 12:00-12:50, Online
Join The Data Lab Community and register for these free events here.