The opinions expressed here are those of the authors. They do not necessarily reflect the views or positions of UK Finance or its members.

Between growing regulatory demands and rising shareholder expectations, financial institutions are turning to artificial intelligence (AI) to improve operations, reduce risk, and stay ahead of the curve.

But here’s the catch: AI is only as smart as the data it learns from.

If banks want to unlock the full power of AI—from faster underwriting to smarter risk analysis—they need one thing first: good data. Without it, AI models can fail, create incorrect outputs, or even put your business’ reputation at risk.

So how can banks build a strong data foundation for AI success? Let’s break it down.

Why data quality matters more than ever

Think of AI like a race car. It may have the best engine and design, but if you put the wrong fuel in it, it won’t get far.

The same goes for AI in banking. Data-related challenges are commonly named as the #1 barrier to AI adoption for financial institutions. Without accurate, consistent, and trustworthy data, even the most advanced AI models can deliver misleading results and non-factual inferences —known as “hallucinations.” These errors don’t just waste time; they can lead to compliance risks and reputational damage.

Five ways to strengthen your bank’s data journey for AI

To truly succeed with AI, banks must take a strategic, organisation-wide approach to data. That means getting serious about how data is sourced, managed, and governed.

Here are five key principles every bank should follow:

1. Sourcing 

Your bank can’t build effective AI models with incomplete or patchy datasets. Banks need access to complete, accurate, and relevant data—which might involve tapping into public, proprietary or even synthetic datasets.

Pro tip:

 Use trusted data vendors that specialise in financial and risk data. These partners can fill in data gaps and support data consistency, generating more accurate insights for better decision-making.

2. Quality

The best AI models are trained on data that’s accurate, timely, and unbiased. Poor-quality data means misleading insights and higher regulatory risk.

To improve quality:

• Consider small, low-risk AI projects to validate data before scaling up.
• Look for vendors using Retrieval-Augmented Generation (RAG) in large-language-models (LLMs), which helps models to retrieve the best data most relevant to a prompt.
   

3. Standardisation

Now, how is the data structured? AI works best when data is consistent and well-organised. Scattered or messy data will slow down workflows and only lead to errors.

What works:

• Connect data along its journey to a grounded entity identifier
• Invest in an end-to-end functional architecture, with a ‘source once, and re-use’ data approach
   

4. Transparency

Ensuring transparency and explainability throughout the data journey is a non-negotiable for responsible AI use.

For reliable and explainable AI outputs, data should be:

• Appropriately defined, with detailed explanations of data elements and attributes
• Supported by the right context, with clear lineage and appropriate metadata
   

5. Governance

Even the best data can go to waste without the right controls in place. Robust governance will help your bank’s AI models be ethical, transparent, and compliant (think GDPR, and BCBS).

Best practices include:

• A governance framework covering each stage of the AI development process, from experimentation to deployment
• Documenting how AI models make decisions
• Regular testing and validation of AI outputs
• Integrating strong data privacy and security protocols 

The bottom line: Don’t bank on AI without good data

AI is set to transform the banking industry, promising a future of faster operations, smarter risk management, and better customer service. But these benefits will only come to banks that treat data as a strategic asset, not as an afterthought.

To recap, a winning AI strategy starts with:

• High-quality data sourcing
• Rigorous data validation
• Well-structured, standardised data formats
• Transparent and explainable data
• Strong governance, privacy and security protocols

Banks that invest in these areas now will be in the best position to turn data into intelligence—and intelligence into action. Because when it comes to AI in banking, it all starts with good data.