Good AI Without Good Data? Don’t Bank On It

In today’s fast-moving world, banks are under pressure to stay competitive. 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 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.

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 primary 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.

To truly succeed with AI, banks must take a strategic, organization-wide approach to data. That means getting serious about how data is sourced, managed and governed. There 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. Use trusted data vendors that specialize 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. Consider small, low-risk AI projects to validate data before scaling up, and look for vendors using retrieval-augmented generation (RAG) in large language models (LLMs). This helps models to retrieve the best data most relevant to a prompt.

3. Standardization. Now, how is the data structured? AI works best when data is consistent and well-organized. Scattered or messy data will slow down workflows and only lead to errors. Connect data along its journey to a grounded entity identifier, and invest in an end-to-end functional architecture, with a source once, and reuse data approach.

4. Transparency. Ensuring transparency and explainability throughout the data journey is a nonnegotiable for responsible AI use. For reliable and explainable AI outputs, data should be appropriately defined, with detailed explanations of data elements and attributes, and 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. Best practices for governance include a framework covering each stage of the AI development process, from experimentation to deployment, documentation of how AI models make decisions, regular testing and validation of AI outputs and the integration of strong data privacy and security protocols.

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.
A winning AI strategy starts with:

• High-quality data sourcing.
• Rigorous data validation.
• Well-structured, standardized 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.