AI and the demands of modern finance

Artificial Intelligence is becoming part of the financial infrastructure that institutions use to help meet regulatory expectations and maintain operational control. It is not being added on top of existing systemsas an optional layer, but incorporated directly into the architecture that supports continuous, multi-jurisdictional operations. This reflects a broader adjustment in how firms are expected to function, especially in environments where stability can’t be assumed, and delays introduce risk.

Financial institutions are now facing pressures that their legacy systems were never designed to handle.Whether tied to payment processing, sustainability disclosures or operational resilience, the underlyingchallenge is the same: existing systems were built for slower, more predictable environments. As institutions move toward architectures that must operate without interruption, AI is being introduced to help maintain reliability at the speeds regulators now require, especially where manual processes alone are no longer sufficient.

Regulatory and operational expectations

The EU’s Instant Payments Regulation (IRP), which came into full effect this year, requires euro-denominated payments to be processed within 10 seconds. This applies at all hours and across all days, without exception for weekends or holidays. This introduces a new continuous operational standard where batch-based controls and fixed processing windows fall short.

Financial institutions can’t afford for core transaction work to pause. It has to run smoothly and uninterrupted at all times, even when volumes spike, or risk conditions change suddenly. That pressure is pushing more firms toward AI-supported systems. These tools can make quick, consistent decisions in situations where people would struggle to keep up. The result is steadier operations and fewer slowdowns, without relying on constant human handling and manual intervention.

By requiring real‑time settlement and sanctions screening under the IPR, the European Central Bank’s regulation effectively makes automation necessary for institutions to comply at scale. However, this does not, and should not, displace the role of human compliance professionals. Instead, automation supports their function by enabling systems to operate at the required speed while preserving institutional oversight.

Changing data expectations in sustainability reporting

Environmental and climate-related disclosures are also shaping how institutions structure their systems. The introduction of the European Sustainability Reporting Standards (ESRS), as well as the proposed climate disclosure rules by the US SEC, signals a shift in how environmental information is integrated into financial supervision. In earlier regulatory models, disclosures were periodic and conventional reporting structures relied on standardised inputs and historical summaries. However, that structure is now being replaced by expectations that require more responsive systems, particularly where financed emissions are involved. These data points change over time, often come from external sources, and may not follow uniform formats. Institutions are now expected to work with data that is both variable andincomplete, while still producing reliable disclosures.

AI can support this shift by enabling systems to incorporate data from multiple sources and assess changing conditions in near real time. This is becoming particularly relevant in ESG risk assessment and investment reporting, where disclosures must be reconciled with local regulatory formats or reviewed at scale.

As supervisory pressure increases and the UN Environment Programme continues to highlight the gap in adaptation finance, supervisory expectations are moving toward outcome-based assessments. AI supports this direction by allowing institutions to engage with uncertain or incomplete data while still maintaining consistency in how decisions are made.

Resilience as an operational baseline

The Digital Operational Resilience Act (DORA) brings further clarity to how operational expectations are being reframed. Under DORA, financial institutions must ensure that critical business functions can continue during periods of disruption, including incidents that originate from external service providers.

To meet this, firms are expected to monitor dependencies and test their response to realistic scenarios.This includes technical, procedural and third-party risks. In many cases, maintaining these controls across distributed systems cannot rely solely on predefined workflows. AI is being introduced to help institutions detect patterns in system behaviour, simulate outage scenarios, and adjust processes based on observed deviations.

Research from the Bank for International Settlements has shown that anomaly detection and predictive monitoring are becoming essential components of operational control frameworks. These systems are not only used during failures, but are increasingly part of day-to-day monitoring, particularly in payment operations and cloud-based infrastructure, where early signals may indicate stress before service interruption occurs.

The focus here is not on making guarantees about continuity, but on demonstrating that risks are being assessed and managed in a timely and proportionate way. In this context, AI supports procedural integrity by ensuring that institutions can respond based on current inputs, rather than static assumptions. This is especially relevant where processes span multiple jurisdictions or rely on third-party providers not directly under institutional control.

Institutional readiness

Institutions that treat AI as an embedded architectural component, rather than a standalone solution, will be better positioned to navigate future regulatory waves. This includes responding to ongoing work by the Basel Committee on Banking Supervision (BCBS), which is examining how AI and machine–learning systems should be integrated into banks’ model governance frameworks. It also aligns with reporting from the International Finance Corporation (IFC) on the governance and implementation of AI in central banking and financial supervision.

The cost of inaction isn’t measured only by financial or operational setbacks. Institutions risk losing trust if delays, errors, or inconsistent controls become visible to clients or regulators. AI, when applied well,helps prevent these exposures by enabling systems to react quickly to changing conditions while maintaining the professional judgment that governance frameworks rely on.

The future of AI in finance

The regulatory and operational context in which financial institutions operate is becoming more complex and less predictable. Supervisory expectations increasingly depend on systems that can function fairly, accountably, with ethical transparency and without interruption, even when conditions shift.

AI is not being introduced to replace existing structures, but to support them where conventional methods no longer provide sufficient reliability. Its role is to help institutions maintain oversight, adapt processes, and apply judgment in settings where scale and timing create constraints.

As these expectations continue to develop, financial institutions that integrate AI into their underlying systems along with the necessary risk assessment and AI-specific governance will be better prepared to meet regulatory obligations and maintain control as conditions change.