How Financial Data Quality Management Powers AI in Banking

For the last couple of years, the spotlight has been all around Artificial Intelligence (AI) and its promise to make commercial banks more profitable and efficient. Expectations are high - according to KPMG’s 2025 Intelligent Banking report, 80% of banks see AI as a source of competitive advantage. Fueled by this optimism, many have moved quickly to adopt AI solutions. Yet, tangible results have been slow to follow.

The reason? AI is only as effective as the data behind it. Without clean, governed, and well-managed financial data, even the most sophisticated models underperform.  Banks eager to scale AI must focus on data quality and governance, not treat them as afterthoughts.

Where banks fall short on financial data quality management

While this may sound simple in theory, managing data is far from easy. Banks are among the institutions handling the largest volumes, and when so much depends on it, things can easily go off track. Here’s why I outlined some of the common mistakes they make with their financial data management that often get in the way of creating a winning AI roadmap?

Skipping the fundamentals

Too often, the excitement around AI causes banks to neglect financial data management. Instead of building scalable, data-first strategies, they jump straight into tools and algorithms - only to face disappointing results later. As our Engineering Director, Peter Ivanov, explains: “You can’t build a successful AI strategy on poor data. And while it’s tempting to dive in with advanced algorithms, I’ve learned that starting with a solid data foundation and scalable infrastructure makes all the difference.”

Shaping the Future of AI: Conversation with Peter Ivanov

This means banks need to ensure they not only have the right data but also know how to use it. Without well-managed data, even the most promising tech initiatives deliver limited results. This often leads to challenges such as:

• Modest ROI from digital transformation efforts, as AI models fail to scale or evolve
• Increased regulatory risk, due to inaccurate reporting or non-compliant data practices
• Poor decision-making, driven by untrustworthy or inconsistent insights
• Higher exposure to fraud, as commercial banks are frequent targets due to the sensitive financial data they manage

Relying on siloed systems

One of the key components of financial quality data management is consistency - yet this remains a common, if not the biggest, challenge for most organizations. Data is often scattered across departments, each relying on its own isolated systems. As U.S. Federal Reserve Vice Chair Michael Barr recently stated: “effective use of AI depends on both data and infrastructure. In practice, however, many banks still rely on siloed systems that aren’t designed for firmwide analysis - making it hard to see the bigger picture and gain a clear, unified view of their data.”

Lack of company-wide strategy

Speaking of the bigger picture, commercial banks struggle to bring all their data efforts together under one clear, company-wide strategy. Until this year, AI adoption was largely fragmented. Banks often pilot solutions like chatbots or document summarization, but these efforts are rarely aligned with broader business objectives. As a result, data remains fragmented , making meaningful banking data analytics difficult.

Fortunately, that’s beginning to change. A January report from the IBM Institute for Business Value found that while tactical adoption was the norm last year - three-quarters of banks surveyed were still in the early stages of deployment - signs of maturity are starting to emerge. Still, most institutions have a long way to go in turning isolated efforts into a cohesive data-driven banking strategy supported by strong financial data quality management.

Difficulties with data governance in banking

Despite the ongoing digital transformation, the banking industry is still held back by outdated legacy systems and fragmented data ownership. Financial data governance is often viewed as a compliance exercise rather than a source of real value. As a result, it focuses more on generating reports than on improving daily data management and usage.

Respectively, important aspects of financial data quality management and alignment with business goals can easily go off track. That makes it harder for banks to scale AI initiatives, make fast, informed decisions, or align teams around a shared, trustworthy data foundation.

Breaking down the barriers: A checklist for CDOs

To get real value from AI, banks must go beyond the technology itself. Success also depends on having a clear financial data management strategy, so here’s where CDOs can take action to remove barriers:

Align data strategy with business outcomes

Before AI can drive real impact, banks need clarity on the data they have and how it supports the business. Start by conducting a financial data audit across departments. This will help you to assess where your data comes from, how it's used, and where issues like duplication, inconsistency, or siloed ownership exist. This creates a shared understanding of your data landscape and highlights which areas are ready for transformation.

From there, pinpoint where AI and analytics can solve specific challenges, such as reducing fraud, streamlining credit decisions, or improving customer personalization. Use those insights to prioritize initiatives that are not only technically feasible but also tied to clear and measurable business goals. This ensures your AI roadmap is built on strong data foundations and delivers value where it matters most.

Redefine how teams work with data

Outdated and fragmented systems remain a major roadblock especially when teams are structured around specific products, departments, or compliance functions, each with their own tools, goals, and reporting.

Instead, focus on standardizing key elements such as data definitions, KPIs, and reporting logic across departments. Centralizing access to validated information through shared platforms can reduce duplication and misalignment. For areas like regulatory reporting, this also means implementing a single source of truth to ensure that all teams work from the same, consistent data - reducing risk and improving audit readiness.

Choose strategic partners

Speed and scale matter. That’s why many forward-looking organizations are shifting away from juggling multiple niche vendors and instead choosing strategic providers who can support the long-term vision. When evaluating potential companies, consider not only their technical expertise, but also their ability to evaluate business objectives, regulatory requirements and operational constraints. A trusted technology partner can provide support across the entire journey - from shaping data architecture and modernizing systems to developing custom software and integrating AI - bringing consistency, clarity, and long-term approach to financial data quality management.

Your Guide on Building Complex Data Analytics Solutions

To wrap up

As commercial banks continue investing in AI, the real differentiator won’t be the technology itself but the quality of the data behind it. With strong financial data quality management, banks can turn innovation into business-driven outcomes.

Partnering with the right strategic vendor can make all the difference. They bring the expertise to implement, scale, and sustain a data-driven banking strategy - all grounded in trustworthy financial data governance.

If you're looking to advance your AI adoption, we are here to support you every step of the way. Now is the time to move from experimentation to execution that delivers.