As the compliance landscape evolves, firms are rethinking the role of technology—not as a one-size-fits-all solution, but as a tool to be woven into the fabric of operations.
According to Corlytics, deploying tech without aligning it with broader governance and workflows risks fragmentation. Instead, the key lies in embedding solutions within a strategic, end-to-end Target Operating Model (ToM) that integrates people, data, and processes across the compliance lifecycle.
One of the clearest indicators of this shift is the changing way firms approach regulatory change management. The journey typically begins in the “firehose” stage, where companies consume vast amounts of raw regulatory data without adequate filtering. This overload creates inefficiencies, overwhelms compliance teams, and makes it hard to act on what matters.
To address this, organisations often progress to the “structured intelligence” stage. At this point, they introduce taxonomies, ontologies and filtering logic to help segment and contextualise information. This reduces irrelevant noise and ensures that updates are more closely aligned with the firm’s specific obligations, business lines, and risk profiles. Manual effort is reduced, and teams can begin focusing on what truly requires attention.
In the most mature phase, regulatory intelligence becomes part of an “integrated governance” framework. Here, obligations are mapped directly to policies, controls, and risk structures. Compliance data supports impact analysis, audit trails, and proactive updates—laying the groundwork for real-time, traceable, and strategically governed compliance. This stage allows firms to move from reactive processes to forward-looking, risk-aligned strategies.
Looking ahead, the integration of artificial intelligence is set to redefine compliance operations entirely. But to benefit from AI, institutions must first build AI-ready ToMs. This includes ensuring that compliance data is centralised, structured, and high quality—creating a solid base for AI to interpret and act on.
Workflow modularity is another key component. Processes should be designed with APIs and modular architecture in mind, allowing AI agents to plug into tasks like monitoring, control testing, or issue escalation. This not only improves efficiency but supports scalability as AI capabilities evolve.
Of equal importance is governance. As AI becomes more embedded in compliance, oversight structures must ensure transparency, explainability, and adherence to evolving standards, such as the EU AI Act. Human-in-the-loop and human-on-the-loop models will be critical to strike the right balance between automation and accountability.
An adaptive ToM also requires continuous learning mechanisms. AI agents should be trained not just once, but continuously, based on enforcement trends, regulatory feedback, and operational outcomes. This creates a feedback loop that strengthens the system over time.
Ultimately, AI-enabled compliance will shift the role of compliance from detective and reactive, to predictive and advisory. Rather than simply responding to change, firms will anticipate it—adjusting controls and providing assurance across all levels of governance, from the boardroom to front-line teams.
To thrive in this new landscape, financial institutions must act decisively. A robust and AI-ready ToM is no longer a competitive advantage—it is the foundation for building a credible, efficient, and agile risk and compliance function in an era of regulatory complexity and digital disruption.
Keep up with all the latest RegTech news here
Copyright © 2025 FinTech Global
Copyright © 2018 RegTech Analyst
