For many compliance professionals, keeping up with regulatory change is like flying a plane while simultaneously building the runway—with a blindfold on. The rules shift mid-flight, and the pressure to stay compliant never lets up.
According to Corlytics, despite this, across global banks, asset managers, and insurers, there’s a shared vision emerging: a shift from reactive, manual compliance to a strategic, data-driven future. It’s what Corlytics calls “compliance nirvana”.
This vision is not simply aspirational. It’s an achievable transformation already underway at leading firms. In this future state, regulatory updates are seamlessly ingested and routed to relevant teams. Obligations are instantly mapped to internal risks, controls and policies. AI proposes changes, while compliance professionals remain in control. Every regulatory step is traceable and prioritised by risk.
One global investment bank has already demonstrated what’s possible. Faced with the SEC’s enhanced disclosure requirements for cybersecurity, the bank implemented AI-powered obligation mapping to connect new regulations with internal policies. Gaps were flagged and escalated to risk owners within 48 hours—a process that previously took weeks.
Three pillars are enabling this transition. First, AI-driven obligation mapping transforms a sea of regulatory documents into structured, navigable intelligence. This is particularly vital under frameworks like Europe’s DORA, which cuts across ICT risk, outsourcing, and operational resilience. Instead of managing compliance in disconnected spreadsheets, firms can now trace regulatory obligations to individual controls in real time.
Second, AI is enhancing rather than replacing human judgment. A UK insurer is using AI to meet Consumer Duty obligations by identifying misalignments between governance controls and regulations. The compliance team then validates the AI’s findings, reducing their assessment workload by half.
Third, regulatory obligations are no longer treated equally. Firms are now layering in enforcement analytics to assess where risk exposure is most severe. A global payments company applied this insight to prioritise workstreams linked to AML compliance in APAC, where enforcement had spiked.
Still, many firms are held back. Legacy systems remain fragmented across GRC, legal, and compliance teams. Staffing shortages and reluctance to trust automation further stall progress. Disconnected workflows and unstructured data make matters worse.
So how can firms start closing the gap? The first step is structuring data—tagging obligations by jurisdiction, risk, and theme. Then, instead of ripping out systems, firms should embed AI into existing workflows. Enforcement intelligence can guide prioritisation, while automation should be applied to low-value tasks, freeing up expert time for strategic thinking. Starting small—perhaps with a single region or process—allows organisations to scale as they go.
Ultimately, compliance nirvana isn’t a fixed destination. It’s a direction of travel, one step at a time, toward greater automation, clarity, and control. As forward-thinking firms continue down this path, others are left to ask: are we ready to take the next step?
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