As regulatory pressure intensifies globally, financial institutions are entering 2026 with a clearer understanding of how artificial intelligence must be deployed across compliance, risk and policy management functions. Insights from 4CRisk.ai highlight a decisive shift away from experimentation and towards scalable, high-ROI AI adoption that can withstand regulatory scrutiny while delivering tangible operational value.
This perspective is shaped by the experience of Supradeep Appikonda, COO and co-founder at 4CRisk.ai, whose career spans decades of deploying complex enterprise software. Appikonda recently delved into the 2026 priorities and why firms should take action with AI regulatory compliance.
During 2025, compliance, IT and cyber teams reassessed their relationship with AI. The initial enthusiasm surrounding large language models gave way to more cautious evaluation, driven by regulatory expectations around transparency, explainability and accountability, Appikonda said. Organisations increasingly recognised that they must be able to explain how an AI-driven decision was reached, trace the data used, and demonstrate meaningful human oversight throughout the process.
At the same time, many teams encountered the limitations of public LLMs, particularly the risks of hallucinations, bias and data leakage. In regulated environments, even marginal inaccuracies can carry significant consequences.
Looking ahead to 2026, the conversation has shifted again. Rather than asking how AI might support compliance, organisations are now focused on selecting use cases that deliver measurable return on investment at scale. AI-powered compliance is transitioning from pilot projects to embedded infrastructure, with success measured in reduced manual effort, faster response times and improved regulatory confidence.
Among the highest-value applications is automated regulatory change management. AI systems can monitor thousands of regulatory sources across jurisdictions, identify relevant changes and map new obligations directly to internal risks, policies and controls. This dramatically shortens response times compared with traditional manual processes.
Another priority is control harmonisation. AI can identify redundant or overlapping controls across multiple frameworks, enabling firms to rationalise their compliance architecture. This “test once, comply many” approach reduces operational burden while maintaining regulatory coverage.
Dynamic policy mapping is also gaining traction. Instead of periodic reviews, AI can continuously assess internal documentation against evolving regulations, flagging gaps as soon as requirements change. This is particularly relevant as new frameworks such as DORA and the EU AI Act come into force.
AI co-pilots are further supporting compliance teams by accelerating research and reporting tasks. While human validation remains essential, AI can draft structured responses, consolidate evidence and prepare regulator-ready documentation in a fraction of the time.
Complaints management represents another emerging use case. AI can classify complaints by risk, jurisdiction and theme, identify systemic issues and map outcomes to regulatory obligations, creating a clear audit trail that supports consistent and defensible decision-making.
Regulators themselves are also becoming more sophisticated users of AI. In 2026, organisations should expect heightened scrutiny not only of AI outcomes, but of model governance, testing methodologies and bias controls. As risk-based frameworks spread globally, firms will need to evidence robust model risk management across all AI-driven compliance processes.
For more insights, read the full story here.
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