AI agents speed banks’ response to regulatory change

AI agents are being used by financial institutions to assemble contract terms, supplier records, financial data and policy history so teams can assess and coordinate responses to regulatory change more quickly.

Regulatory updates can trigger work across finance, procurement, legal, HR, operations and IT. Firms must identify affected contracts, review supplier and customer relationships, update processes, and produce new reporting. Locating the right information across multiple systems and connecting consequences across functions can delay action.

Agents are typically connected to enterprise data stores and document repositories where they index contracts, invoices, HR records and policy documents. Once linked, agents can flag contracts with clauses affected by a rule, list suppliers that need review, extract transaction records and identify gaps in reporting data. They can also produce standardised summaries and action lists for teams to review.

Key stakeholders in these projects include compliance officers, legal counsel, finance teams, procurement and vendor managers, HR, IT, operations and internal audit. Senior management and the risk function often set priorities and approve resource allocation. Early involvement of these groups is used to define data sources, set decision rules and establish handoffs for remediation work.

A common end-to-end pattern begins when a rule change is detected or announced. Agents run searches across contracts and records and produce a ranked list of affected items. Teams receive concise summaries and data extracts for rapid review, required reporting elements and system updates are identified, tasks are assigned and tracked, and controls and templates are updated to meet the new requirements. Continuous monitoring can trigger new searches when regulators issue further guidance or enforcement actions.

Panelists at a recent webinar hosted with Workday recommended prioritising high-risk rule changes for initial automation, ensuring agents can access canonical data sources, and defining clear approval workflows so subject-matter experts retain final authority. They described the main operational effect as a reduction in time spent assembling evidence and coordinating responses across departments.