Good AI without good data? Don’t bank on it

Looking to solve your operational inefficiencies with artificial intelligence (AI) but lack good-quality data? Don’t bank on it.

In the highly regulated, systemic industry of banking, rolling full steam ahead with an AI initiative using patchy, inconsistent data could lead to disappointing outcomes. After all, AI is only as powerful as the data it learns from - AI models require good, high quality data to provide reliable and productive outputs. Echoing the IT phenomenon GIGO (‘garbage-in, garbage out’), early releases of generative artificial intelligence (GenAI) chatbots based on large-language-models (LLMs) were prone to 'hallucinations' – 'nonexistent inferences' due to the lack of factual data.¹

Without good data, there is a higher risk of AI models generating misleading insights - leading to poor decision-making, regulatory and compliance risks, as well as reputational damage. Good data, then, is not just a technical necessity - it's a business imperative.² If you're looking to future-proof your organization with AI, start with your data.

Where are we now?

AI is rapidly reshaping the global banking landscape, promising a new world of efficiency and productivity gains across mission-critical workflows. While banks have eagerly explored AI applications in risk assessment, compliance and fraud detection, scaling these initiatives remains a challenge.³ Data management is the primary hurdle, with many banks facing data-related challenges and risks as their top barrier in AI adoption.⁴

As industry consolidation accelerates, the stakes are even higher. Banks are burdened with outdated IT systems, siloed information and undigitalized, unstructured data across traditional (loan applications, general ledger) and new real-time sources (e.g. mobile apps). What’s more, they also face the challenge of navigating technological innovation in an evolving regulatory landscape, as well as ensuring compliance with data management frameworks such as those from the Basel Committee on Banking Supervision (BCBS) and General Data Protection Regulation (GDPR). These challenges and regulatory requirements mean that a holistic and comprehensive data strategy is essential for banks to fully unlock AI’s potential. Good data is not just a static outcome for your business – it’s a continuous journey that touches every part of the organization, demanding a commitment to excellence at every step.

Optimizing your data journey for AI excellence

So, what qualifies as good data? We’re glad you asked. Achieving good data for AI excellence is not just about having large volumes of information. It’s a sustained top-down commitment to the data journey through your organization, ensuring the highest standard of data availability, quality, standardization and governance throughout.

Any successful AI implementation in banking hinges on a strong data strategy, guided by the following principles:

1. Sourcing: The first step is simple – you need the right data. This means complete, representative datasets, with no gaps or missing pieces. Trusted vendors with well-curated datasets can enrich AI inputs, making insights sharper and decision-making smarter.   
2. Quality: High-quality data is a non-negotiable – data should be verifiable, bias-free and reflective of the real world. Alongside partnering with reliable data vendors, consider validating data with low-risk AI projects before scaling up.   
3. Standardization: Now, how is your data structured? For reliable results, data must be well-organized and error-free. Investing in an end-to-end functional architecture, where data can be sourced once and reused, will be a key facilitator of better data management.   
4. Transparency: The highest standards of transparency and explainability should be upheld throughout the data journey. Ensuring that your data is supported by the right context – including clear data lineage and the appropriate meta-data – will be key.   
5. Governance: A robust governance process is vital for any safe and responsible AI implementation. This should cover the entire AI development process from experimentation to deployment, from the integration of strong security protocols to the regular testing and validation of AI outputs.  

Download the full e-book to find out more about the five key principles of good data, and how your bank can optimize the data journey for AI excellence.

References

1. CIO, ‘How to ground AI models with high-quality business data’, accessed May 1, 2025

2. Forbes, 'AI Is Only As Good As The Data Behind It—And What You Can Do About It', accessed May 5, 2025

3. EY, ‘Five priorities for harnessing the power of GenAI in banking’, accessed May 3, 2025

4. Bank of England, ‘Artificial intelligence in UK financial services – 2024’, accessed May 4, 2025