When the financial industry talks ‘agentic AI’, there’s a tendency for the conversation to quickly devolve into cutting-edge technologies – large language models (LLMs), neural networks, generative algorithms (GenAI) etc.
Agentic AI is really about transforming the business processes that define firms’ operations and the roles that supervise them. Success is dependent on more than just the tech stack. As agentic AI becomes mainstream, overlooking the process and people perspectives puts transparency and accountability at risk, just when regulators are demanding transparent, auditable process oversight.
Firms must ensure that AI doesn’t only run smarter but also runs transparently, accountably, and demonstrably aligned with regulatory expectations and business KPIs. The European Union’s AI Act clearly emphasizes transparency and accountability in processes utilizing AI, mandating traceability, human oversight, and explainability.Traceability involves maintaining detailed records of each decision or action made by an AI agent, enabling auditors and compliance teams to reconstruct exactly why a decision was made.
Explainability must provide human-readable explanations or rationales for significant AI-driven actions, enabling Chief Compliance Officers (CCOs) and other accountable executives to understand and explain the underlying logic behind decisions to supervisors on demand.
From Static Automation to Dynamic Orchestration
Traditional process management has been based largely on Robotic Process Automation (RPA) and Business Process Management (BPM). These tools have proven effective at automating repetitive tasks and structuring workflows in predictable environments. However, these approaches are rule-based and inherently static, requiring manual reconfiguration to adapt to changes in regulatory mandates.
Enter agentic AI – a significant leap from these conventional tools. Agentic AI enables dynamic orchestration, where AI agents autonomously manage entire workflows, adapt in real-time, and handle exceptions without manual intervention. Unlike RPA bots, which follow rigid scripts, AI agents evaluate context, make autonomous decisions, and optimize processes continuously.In one recent example, Fenergo’s FinCrime Operating System introduces an innovative agentic AI layer that redefines AML and KYC processes. At its core are six autonomous AI agents – ranging from Data Sourcing and Screening to Document and Insights Agents – each designed to execute discrete financial crime tasks across the client lifecycle. For example, the Screening Agent continuously monitors new sanction lists and adverse media sources, automatically resolving low risk matches and escalating exceptions for human review. Meanwhile, the Document Agent uses generative AI to classify, extract, and link onboarding and KYC documents, drastically reducing manual errors. Behind the scenes, a role-based Command Centre provides compliance leaders with real-time dashboards, detailed audit trails, and transparent agent logs – ensuring every decision remains traceable, explainable, and fully aligned with regulatory expectations.
In another example, Oracle Financial Services has embedded agentic AI capabilities into its Investigation Hub Cloud Service to revolutionize financial crime investigations. AI agents autonomously gather evidence, analyse alerts, and generate detailed case narratives – transforming what was once a manually intensive process into a streamlined, automated workflow. For example, the Screening Agent continuously monitors transaction data against sanction lists and anomalous patterns, while generative AI (GenAI) crafts structured narratives that clearly explain each alert’s context and relevance. Investigators can then review prioritized cases that come complete with comprehensive documentation and decision reasoning. The result is a significant reduction in false positives and investigation time – freeing professionals to focus on high-stakes cases – while maintaining full auditability with every action logged, timestamped, and traceable in real time.
The Future of Process Innovation
As agentic AI becomes mainstream, regulators will increasingly scrutinize how AI agent-driven decisions align with established regulatory frameworks. They have made it clear that regulatory obligations are intended to be technology-agnostic and that firms remain accountable, irrespective of the technology used. The future compliance landscape will likely see standardized agentic process templates for critical operations such as financial transactions, HR onboarding, and customer engagements. Such templates would include built-in compliance checkpoints, governance-as-code practices, and ethical guardrails embedded directly within process models.
This evolution promises significant opportunities for innovation in process governance. Real-time dashboards, AI-driven compliance monitoring, and dynamic KPI tracking will become the norm. For businesses, the task now becomes ensuring they invest not just in AI technology but also in robust process governance capabilities that satisfy regulatory scrutiny.The AI narrative must evolve beyond mere technological excitement to recognize the deep implications for business processes. For compliance professionals and process designers, the future hinges not only on understanding AI’s potential but also on mastering the orchestration and governance of agentic workflows. Compliance must have a seat at the table as active participants in agentic-AI process design and governance, ensuring alignment with both business KPIs and regulatory mandates.
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