A Faster Path to Scaling GenAI in Banking Compliance

Compliance is entering a new stage. The demands on control functions have grown sharply, and traditional methods—built on manual review and fragmented data—are reaching their limits. Generative AI (GenAI) offers a practical way to close that gap, bringing greater consistency, speed, and visibility to complex, data-heavy work.

Many banks are exploring these capabilities through pilots and proofs of concept. Progress has been careful, shaped by valid concerns around governance and regulation. Yet hesitation carries its own risk. As GenAI becomes embedded in more core processes, those that delay will find themselves playing catch-up on both capability and cost.

The question now is how to move forward with confidence. The banks making progress are applying GenAI to specific, high-value problems, using the results to strengthen—not replace—human judgment. Their experience shows that responsible adoption is possible and that the gains extend well beyond efficiency.

Banks Are Moving Too Slowly—And It’s Costing Them

GenAI’s potential in risk and compliance is already proven. Yet most banks remain stuck in early-stage adoption—running pilots that never scale. BCG research shows that roughly three-quarters of institutions are still experimenting, and fewer than one in ten have measurable use cases in operation. (See Exhibit 1.)

A cautious “test and learn” approach can seem sensible given regulatory and technical risks. But meaningful returns come only when banks move beyond experimentation and scale proven use cases. One large retail bank shows what that looks like. It first used GenAI to improve fraud detection. Once the model consistently met its targets, leaders expanded it across business units. Within a year, false positives fell by 40%, freeing compliance teams to focus on high-risk cases.

Another global bank went further, embedding GenAI into its credit risk models. By using large language models to analyze unstructured data from financial reports and market updates, the team compressed multi-week portfolio reviews into a few focused days. That speed gave risk managers earlier visibility into deteriorating exposures and let them adjust limits before losses grew. The shift also trimmed manual effort and data-handling costs across the risk function.

But these banks are the outliers. Today, only about 5% to 10% of banks have a well-funded GenAI strategy. Most remain in exploration mode—a phase that limits results and gets more expensive the longer it lasts, given the continued resource use and lack of scale. As GenAI tools mature, the performance gap between early adopters and laggards is set to grow sharply. The institutions moving faster are now widening the divide.

BCG research found that risk and compliance use cases top the list in terms of ROI potential. Scaling GenAI across compliance and risk cuts manual review time, improves detection accuracy, and strengthens model governance—advantages that compound with each new application. Early adopters are also better prepared for future regulation of AI, since they are already building traceability, audit trails, and human-in-the-loop controls into their systems.

From Pilots to Progress

The next step for banks is clear: move from experimentation to execution. Two applications are already showing tangible value in risk and compliance. They demonstrate what scaled GenAI can look like in practice—and how quickly benefits compound once deployed across the enterprise.

Automating KYC at Scale

Know Your Customer (KYC) remains one of the most resource-intensive parts of compliance. Verifying and updating customer data across multiple systems can consume thousands of hours each year, and even small errors risk regulatory penalties. GenAI is already helping banks ease this burden while improving accuracy and speed. Examples include:

• Dynamic data extraction. GenAI solutions automatically gather and reconcile data from public registries, transaction histories, news feeds, and unstructured sources—dramatically reducing manual review work.
• Real-time risk scoring. GenAI tools can update client profiles as new data appears—unlike traditional risk scoring models that rely on fixed inputs—giving compliance teams a current view of exposure and reducing the lag between risk identification and action.
• Automated file closure. For lower-risk clients, GenAI applications generate summarized justifications and complete file reviews autonomously, freeing analysts to focus on exceptions and high-risk cases.
• Agent-led support. AI agents track corporate filings, regulatory updates, and media sources to keep client records current and alert teams to relevant changes, ensuring client profiles remain current and comprehensive. (See “How Agents Improve Transaction Monitoring.”)

A leading European bank cut KYC-related costs by 20% and improved file-closure rates by 67% within a year of deploying GenAI solutions that automate and manage workflows at scale. Freed from repetitive tasks, compliance staff were redeployed to higher-value investigations and oversight—delivering both better outcomes and stronger engagement. (See Exhibit 2.)

Streamlining Document Management

For many banks, document management is a quiet killer—so ingrained in day-to-day operations that inefficiencies go unnoticed. Yet every onboarding delay, contract dispute, or compliance review often traces back to one source: fragmented, hard-to-access documents.

One global systemically important bank decided to treat the issue directly. Instead of launching isolated GenAI pilots, it built a single, enterprise-wide document engine that could serve legal, risk, and compliance functions alike.

The system doesn’t just store files—it interprets them. Using retrieval-augmented generation and language models, it can read contracts, extract key terms, summarize exceptions, and auto-populate required fields. Employees can query it in plain English—“Show me the latest term sheet” or “Highlight changes in the counterparty credit agreement”—and receive context-rich answers drawn from across systems.

The results were immediate. Productivity rose by roughly 20% to 25%, errors and duplicate work fell, and so did the hidden toil of “find and format” work. With version control, audit trails, and policy alignment built in, compliance confidence also increased.

The system was designed for scale. Each new document category that was added to the system—from credit agreements to risk memos—made the tool more useful across the business. What began as a fix for onboarding became a $100 million efficiency engine, with a projected $1 billion path to value as new use cases come online.

Getting Started with GenAI in Compliance

For banks still in pilot mode, the priority is simple: start moving. The path to scale begins with small, targeted wins that build confidence and control. The following steps can help turn early experiments into business results.

Start with one problem that matters.

Begin with a clear business problem that offers measurable impact and quick results. Pick a process that’s slow, costly, or error-prone—and where data quality is strong enough to make automation credible. Solve it first, prove the benefit, and use that proof to build support for the next use case.

Show progress quickly.

Don’t wait for a perfect model. Share early results that demonstrate tangible improvement, such as faster reviews, cleaner data, or fewer manual steps. Visibility builds trust and maintains momentum.

Build control as you go.

Define GenAI’s role and boundaries from the start. Document how GenAI decisions are made, setting confidence thresholds for predictions, identifying points where human judgment must take over, and scaling oversight proportionate to the complexity of the use case. A clear audit trail satisfies regulators and helps scale with confidence.

Modernize the data and tech base.

Build a connected data and technology architecture that gives teams seamless access to core systems. Use modular, cloud-based infrastructure, open APIs, and a hybrid language model strategy that combines large and specialized models tuned to compliance—and embed governance and security controls from the outset. The more consistent the foundation, the faster each new use case can be deployed.

Prepare people for new ways of working.

Equip teams to collaborate with GenAI, not just oversee it. As BCG’s 10-20-70 framework suggests, about 10% of the impact from AI comes from algorithms, 20% from tech and data, and 70% from people and processes. Redesign roles, retrain staff, and integrate automation into daily workflows.

GenAI has crossed the threshold from hype to operational necessity. Early movers are already reshaping compliance, turning once-unwieldy processes into competitive advantages. These leaders are redefining how risk is managed and setting new standards for agility and resilience. Others can learn from these successes and begin claiming the benefits. For leadership teams, the task now is to identify the processes where GenAI can deliver lasting value, scale them with the right guardrails, and make them part of how compliance actually works. Those that do will gain a more adaptive, resilient control function.