Overview
A leading global digital bank's Risk & Controls team was struggling to keep pace with product innovation. Every new feature required comprehensive compliance review—a manual, labor-intensive process that took 2-3 days per product and created significant launch delays.
Control specialists spent the majority of their time on repetitive documentation work: reading product specifications, researching AML regulations, searching for adverse media at peer institutions, cross-referencing internal policies, and compiling reports. With engineering teams shipping new features weekly, the bottleneck was unsustainable.
Beyond product reviews, the team faced another operational challenge: maintaining a centralized control testing repository. Control findings were scattered across Jira tickets, Confluence pages, and email threads. Consolidating these findings, removing duplicates, and tracking remediation status required hours of manual data entry into Airtable each week.
The team needed to scale their control testing capacity without adding headcount, while maintaining—or improving—the rigor and consistency of their risk assessments. That's when they found StackAI and built a suite of risk and controls agents, resulting in:
95% faster compliance reviews: Product assessments reduced from 2-3 days to 30 minutes
100% automation of control validation: Eliminated 3-4 hours/week of manual review
5x capacity increase: Team now handles 15+ product reviews per week vs. 3 previously
30% cleaner control inventory: Duplicate controls identified and eliminated in first 90 days
The Problem: Product launches delayed by 2-3 day manual compliance reviews
Every new payment feature or product required comprehensive compliance review across multiple risk domains. A control specialist would spend:
4-6 hours reading product specifications and researching AML regulations
4-6 hours searching regulatory databases and news sources for adverse media about similar products at peer institutions
8+ hours synthesizing findings into an executive-ready risk assessment report
This 2-3 day process created launch delays and limited the team to reviewing just 3 products per week.
The Solution: Multi-agent workflow delivers comprehensive compliance reports in 30 minutes
The team built a multi-agent workflow that automates the entire compliance review:
AML Control Assessment: An AI agent reviews product specifications against the bank's complete AML control framework—including Bank Secrecy Act requirements, FinCEN guidance, and internal control standards. The agent identifies control gaps (e.g., insufficient transaction monitoring, weak velocity controls), cites specific regulations, and recommends fixes.
Adverse Media Monitoring: A second agent searches financial regulatory databases, consent orders, and industry news for control failures at peer institutions related to similar products. It identifies patterns in peer control deficiencies, summarizes regulatory enforcement precedent, and flags reputational and regulatory risks.
Comprehensive Report Synthesis: A final "control auditor" agent consolidates both assessments into an executive-ready risk report containing:
Executive summary with overall control effectiveness rating
SWOT analysis (control strengths and gaps)
Detailed control gap assessment by risk category
Recommended control enhancements prioritized by severity
Control approval status and remediation roadmap
The completed report is automatically delivered to SharePoint for further review.
The Problem: Inconsistent control documentation and duplicate controls across the organization
As teams across the organization drafted new controls for products, systems, and processes, the Risk & Controls team faced three critical challenges:
Inconsistent quality: Control descriptions varied wildly in quality—some were vague and unactionable, others lacked proper risk linkage or didn't follow the bank's control framework standards
Manual validation: A senior control specialist spent 3-4 hours per week reviewing newly drafted controls against the bank's "gold standard" control library, identifying gaps, and providing feedback to control owners
Duplicate controls: Multiple teams often created similar or overlapping controls without realizing they already existed, leading to redundant testing, inconsistent implementation, and a bloated control inventory
This manual review process created delays in control implementation and made it difficult to maintain a clean, standardized control environment.
The Solution: Agentically validating controls against gold standards
The team built an AI agent that automates control validation and standardization. If a control doesn't meet standards, the agent rewrites it to align with the gold standard format while preserving the original intent. For example, a vague control like "Monitor transactions for suspicious activity" becomes "The AML Operations team reviews system-generated alerts for transactions exceeding $10,000 or meeting structuring patterns daily, documenting review decisions in the case management system within 24 hours."
Deduplication Detection: Before finalizing a new control, the agent searches the existing control inventory for potential duplicates using semantic similarity matching. It identifies controls that:
Address the same risk with similar mitigation approaches
Apply to the same systems or processes
Have overlapping ownership or execution frequency
When duplicates are found, the agent flags them for the control owner to either retire the new control or consolidate with the existing one.
Conclusion
This Risk and Controls team achieved transformational efficiency gains while improving quality and consistency. Product compliance reviews dropped from 2-3 days to 30 minutes, a 95% reduction that enabled the team to increase weekly capacity from 3 to 15+ product reviews without adding headcount. Control validation, which previously consumed 3-4 hours of senior analyst time each week, is now fully automated, ensuring every control meets gold standard requirements before approval. The deduplication capabilities identified and eliminated 30% of redundant controls in the first 90 days, significantly cleaning up the control inventory. Currently deployed across 4 risk and control teams (AML, Credit Risk, Operational Risk, and Fraud Controls), the team is now continuing to work with StackAI to expand use cases to 50+ teams across product, operations, and technology in Q1 2026 and add specialized agents for vendor risk assessments, regulatory change analysis, and continuous control monitoring.
Want to see how StackAI can power your firm's AI transformation? Get a demo here.
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