Turning compliance into a competitive edge for AI in financial services

By Rishi Kapoor, Head of WW Partner Sales Engineering & Solutions: Technology & Innovation Partners at Alteryx

According to the Bank of England, 75% of UK financial services firms are now using AI in their operations. However, adoption alone doesn’t mean success. Extracting value from AI while operating under some of the world’s strictest data regulations is what will separate leaders from laggards in the sector.

Between the EU AI Act, GDPR and increasingly complex data sovereignty requirements, European banks and financial institutions face significant constraints on how strategically they can use their data. These regulations, while essential for customer protection, often prevent firms from moving fast enough to embed AI into core decision-making and operations at scale.

As the sector enters the next phase of enterprise AI, governance must move from being a perimeter function to being built directly into the data and analytics environment itself.

Legacy compliance models are incompatible with modern AI

The regulatory frameworks governing AI and personal data – particularly the EU AI Act and GDPR – represent a fundamental shift in expectations. The EU AI Act introduces risk and capability-based obligations, requiring greater transparency, documentation and human supervision for higher-risk AI systems. These rules sit alongside GDPR, which imposes strict rules on the processing and reuse of personal data across AI and analytics workloads.

Together, these regulations are exposing the limitations of compliance models built on restrictive access controls, manual approvals and segmented teams. While effective in earlier data environments, these approaches struggle to support AI initiatives that rely on free-moving data, automated governance and compliance that scales without slowing delivery.

Without modernisation, firms trying to scale AI inevitably encounter friction that slows innovation and increases operational risk.

Enabling compliance with AI-ready data

A growing number of financial services firms are integrating governance directly into their data platforms and workflows. Rather than treating compliance as a barrier, they are embedding it at the data layer where AI models are designed, trained and deployed.

Take the example of a risk or fraud analyst building an AI-assisted transaction monitoring workflow. Embedded governance at the data layer allows them to work with sensitive transaction data, trigger automated controls for personal data, and document model inputs and decisions as part of the workflow itself. This reduces approval cycles, while giving risk, legal and compliance teams full visibility into how their data is accessed and used.

To enable this, firms are adopting prioritising AI-ready data that acts as a neutral, user-friendly software layer across multiple data sources and provides governed access to AI while applying compliance measures consistently. For European financial services institutions, this approach allows regional and sector‑specific rules to be built into AI workflows, greatly simplifying sign‑off and reducing compliance ambiguity.

Building trusted decision-making

By embedding governance into the data environment, every function across a financial institution benefits. Analysts can modernise operations with confidence, risk and compliance teams can review and approve AI use cases more quickly, and everyday business users are empowered to apply AI within their own processes without compliance concerns.

The result is an AI strategy that is more collaborative, more transparent and aligned with enterprise-wide objectives. Instead of slowing teams down, compliance becomes a structural advantage, creating an environment where AI adoption, from non-technical users to analysts, accelerates responsibly.

Turning compliance into advantage

When governance is built into data platforms, financial services are better equipped to operate effectively in the AI era. Not just adopting or “doing AI” but delivering successful use cases and ROI. This approach enables secure data access, scalable compliance and faster AI deployment. By embracing AI-ready data, firms can unlock the full value of AI while confidently meeting evolving regulatory expectations.