By Kalyani Ramadurgam and Samah Chowdhury
Compliance staff and AI tools work best in tandem — technology handles large-scale manual work while people provide context and judgment.
As banks navigate an increasingly complex regulatory landscape, the pressure to do more with less is intensifying. The regulatory burden increased significantly under the Dodd-Frank Act, which nearly doubled the number of regulations for U.S. banks and added approximately $50 billion in annual compliance costs for the industry. Against this backdrop, artificial intelligence is poised to transform compliance operations. Compliance work largely consists of skilled professionals spending countless hours reading, analyzing and documenting their review of complex materials — exactly the type of cognitive tasks that modern AI excels at augmenting.
Current state of bank compliance
Banks face a host of challenges that make traditional compliance processes increasingly unsustainable. From consumer protection to anti-money laundering, the cost and complexity of compliance is escalating. Legacy systems and labor-intensive manual reviews only add to the burden. The following issues illustrate the strain that banks are under:
- Labor-intensive reviews of vendor documentation and contracts;
- Regulatory changes that require constant monitoring and adaptation;
- Internal audit processes that require extensive documentation and cross-referencing;
- Legacy systems that struggle to scale with growing transaction volumes; and
- Error-prone and inconsistent manual reviews that pose larger risks when missed.
Given these challenges, AI presents an opportunity to modernize compliance without compromising on rigor.
AI offers additional bench strength
The nature of compliance work — processing vast amounts of data, identifying regulatory gaps and ensuring adherence to complex frameworks — makes it particularly well-suited for AI integration. By automating routine tasks, AI can handle the heavy lifting, leaving compliance teams to focus on higher-value activities. Because compliance operations are largely behind the scenes, AI can be seamlessly integrated without disrupting customer-facing services.
For example, intelligent document analysis tools can rapidly process and extract critical data from documents, streamlining and enhancing accuracy in review processes. These tools can identify key clauses and potential gaps in contracts, cross reference vendor documents with internal policies to generate preliminary risk assessments and flag regulatory discrepancies across different jurisdictions. By minimizing manual errors and accelerating review cycles, banks can reallocate human resources toward strategic oversight and risk management.
AI offers compliance a number of streamlining capabilities. With automated documentation generation, AI tools can create preliminary drafts of key compliance reports, policies and audit findings, allowing teams to focus on decision-making and strategic oversight. AI, via continuous monitoring and updates, can track regulatory changes in real time and automate testing, ensuring that compliance measures remain up to date and effective. Automated testing can compare deliverables against internal standards and procedures, immediately flagging deviations. This shift to a proactive, technology-driven approach can ensure that regulatory and security demands are met with greater efficiency and accuracy with proper oversight.
Automate the process, not the principle
While AI offers tremendous potential to streamline compliance workflows, it’s crucial to strike a balance between automation and oversight. Effectively harnessing AI means not losing sight of a core tenet of compliance: maintaining independent judgment and credible challenge to business decisions.
A prudent step in AI adoption is back-testing. For example, a bank might feed its AI tool a set of historical examination findings or regulatory exam manual case studies and compare the results against human examiners’ findings. This helps reveal whether the AI tool correctly identifies key issues and flags potential red flags. In another scenario, a compliance officer can intentionally throw tricky scenarios at the AI model — such as Bank Secrecy Act/anti-money laundering alerts or fair lending cases — where contextual judgement is necessary. This kind of back-testing can reveal whether the tool’s output aligns with real-world regulatory expectations and the bank’s compliance strategy.
Banks that successfully integrate AI into their compliance operations tend to follow the mantra: Automate the process, not the principle. They automate what they can to gain efficiency and accuracy, but they uphold the principles of thorough oversight, ethical conduct and accountability. Early movers are already seeing results.
Emily Kolm, senior manager of enterprise risk at Omaha, Nebraska-based American National Bank, says: integrating AI into her institution’s framework helped the bank “gain the ability to transform how we analyze third-party relationships, improving our capacity to evaluate and address risks.” Her team used Kobalt Labs to enhance efficiency in identifying potential vulnerabilities — a shift that Kolm says enabled a more proactive and data-driven approach to risk management.
This kind of implementation reflects a broader trend: Banks are treating AI as a compliance co-pilot, not a replacement.
Making AI work for compliance
Infographic developed with generative AI tool ChatGPT.
Infographic developed with generative AI tool ChatGPT.
For banks looking to modernize their compliance operations, a strategic roadmap is key — especially as regulatory scrutiny and operational complexity increase.
Start by focusing on areas where AI can have the most immediate and measurable effect — such as documentation-heavy processes where manual reviews slow operations and regulatory expectations are clearly defined. These workflows are prime candidates for automation. From there, select AI solutions that are adaptable and capable of learning from team input, integrating with existing systems and scaling alongside evolving regulatory requirements. Finally, implement strong governance and guardrails by setting clear policies on who can use AI tools, how outputs are reviewed and where human judgment remains essential. This ensures AI enhances compliance efforts without compromising oversight or accountability.
Lincoln Savings Bank, a midsize community bank based in Reinbeck, Iowa, tested multiple solutions. They piloted an AI tool from Kobalt for compliance and third-party oversight and reported measurable improvements. The bank’s risk and compliance team reported an accurate and thorough identification of compliance gaps, as well as a reduction in review time per document from multiple days to 15 minutes, with an improvement in accuracy.
“Leveraging AI through Kobalt is allowing us to significantly expand our risk oversight without the addition of resources,” says William B. Peek, chief risk officer at Lincoln Savings Bank. “AI doesn’t make the risk decision but helps our team see the more critical and pertinent details while eliminating low value, non-value-added noise that consumes capacity.”
The integration of AI into compliance operations represents a fundamental shift in how banks manage risk and adhere to regulatory standards. This will require all risk professionals to be conversant with AI as a tool, its capabilities and its limitations. With thoughtful implementation, the results are well worth the investment — freeing up teams to focus on higher-value work, improving accuracy and enabling a more agile response to regulatory change.
Kalyani Ramadurgam is CEO of Kobalt Labs. Samah Chowdhury is senior director of innovation strategy at ABA.
