Artificial Intelligence

How to Leverage AI in Financial Services Regulatory Compliance

Banks and other institutions are eager to make use of the technology to lighten their burden, but they should be careful in doing so.

Aaron Cheiffetz

Aaron Cheiffetz is Executive Strategist and Field CIO of Financial Services for CDW.

Matt Sickles

Matt Sickles is a techno-futurist and an executive strategist advising the financial, healthcare and nonprofit sectors at CDW.

Regulatory compliance has always been a heavy lift for financial institutions. Whether it’s know your customer (KYC) guidelines, anti–money laundering or emerging frameworks such as the Cyber Risk Institute profile, the demands on banks and insurers continue to grow. It’s no wonder, then, that institutions are responding enthusiastically to the opportunity to leverage artificial intelligence to make compliance faster, more accurate and continuous.

We’re seeing a rapid evolution in how AI is deployed across compliance workflows. Rather than bolting on AI as a feature or product, financial institutions are beginning to embed the technology directly into the fabric of their operations. It’s becoming native to their surveillance systems and KYC processes. That’s a fundamental shift, from AI as a tool to AI as part of the institution’s core nervous system.

Compliance Moves From Static Rules to Adaptive Intelligence

Traditionally, compliance systems have been based on static, rule-driven models: If a transaction exceeds a certain amount of money or originates from a flagged geography, it is automatically tagged for review. The problem: That generates a lot of false positives. Analysts have spent an enormous amount of time reviewing alerts that have ultimately proved benign.

Modern AI models are different. They learn from context, not just thresholds. They detect subtle relationships among entities, accounts and behaviors that static systems miss. That adaptability dramatically improves anomaly detection and fraud prevention while reducing the human workload.

We’re even seeing what we call “AI versus AI” dynamics — fraudsters using generative tools to conceal patterns, while institutions deploy their own machine learning models to surface them. The next competitive advantage won’t come from who can write more rules, but from who can train smarter models.

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Transparency: The Key to Regulator Trust

One of the biggest questions in financial AI adoption is transparency. Regulators don’t just want to know that a model works, they also want to understand why it reached a particular conclusion.

Generative models can translate qualitative questions into quantitative answers. AI can comb through enormous volumes of unstructured information — policy documents, risk models, audit reports — and classify and align them to regulatory standards such as those from the Financial Industry Regulatory Authority, National Institute of Standards and Technology and others. This capability allows institutions to show regulators not just that they are compliant, but how they maintain compliance.

That’s a major leap forward. Instead of conducting point-in-time assessments — annual or semiannual reviews that are outdated the moment they’re completed — institutions can maintain an “always-on” assessment model. AI engines can continuously monitor compliance documentation, risk indicators, and transaction activity across systems such as Microsoft Teams, SharePoint and Salesforce.

This omnipresent view turns compliance from a reactive process into a living, predictive function. When AI can instantly assess whether a new pattern is a threat or simply a trend, compliance becomes a source of insight and resilience rather than a regulatory burden.

DIVE DEEPER: CDW can help your financial services break down data silos.

We’re already seeing large financial institutions put these principles into action. For example, organizations participating in the Cyber Risk Institute consortium are discovering their annual risk assessments no longer provide sufficient value. The lag between completing and reporting on these assessments — often 90 days or more — means that many findings are obsolete by the time leadership reviews them.

By adopting AI-based tools such as Cortex by Palo Alto Networks or Cisco’s emerging natural language query platforms, institutions can now perform near real-time risk analysis. They can correlate threat indexes to regulatory frameworks on demand and flag exceptional risks instantly.

Instead of waiting for quarterly readouts, organizations can treat risk as a continuously monitored metric. That shift alone represents a major modernization in financial governance.

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Get Started on AI Financial Compliance

Most institutions are somewhere along the AI compliance maturity curve. Some are just starting to explore automation opportunities, while others are already integrating AI across enterprise risk management. Regardless of where an organization sits on this continuum, we see three foundational requirements for success:

AI-driven decisions must be traceable and testable. Institutions need to document inputs, outputs and human overrides so they can reproduce results for auditors and regulators.
There must always be a human in the loop, a responsible owner who validates and approves model behavior.
Many firms are establishing AI Centers of Excellence or governance boards that bring together leaders from IT, risk and finance divisions. This cross-functional oversight ensures models are deployed responsibly and align with both business outcomes and regulatory expectations.

We typically guide clients through a four-phase process:

Discovery. In this phase, we align on the client’s problem statement and desired outcomes.
Assessment. Here, we collect data, benchmark against industry standards and identify maturity gaps.
This phase involves designing solutions and visualizations (such as heat maps of compliance risk), paired with a roadmap for improvement.
Execution. This is where we deliver an operational blueprint or pilot outcome report, and, if desired, work with the institution to build the implementation.

One question we hear from clients is how far they can get with off-the-shelf tools before needing custom AI development. The answer varies, but in our experience, financial institutions can usually address about 80% to 85% of their compliance automation needs with commercially available platforms from providers such as Palo Alto Networks, Cisco or CrowdStrike. The remainder involves proprietary models trained on internal data or focused on highly specific risk scenarios.

CDW can help financial institutions navigate every stage of their AI compliance journey, from infrastructure design and data readiness to model deployment and governance. Our experts combine deep regulatory insight with technical expertise across leading AI and security platforms, helping clients modernize compliance programs with confidence, transparency and control.

This article is part of BizTech's EquITy blog series.