Security

Financial Services Are Using Artificial Intelligence to Navigate Regulations

In five key areas, AI and machine learning tools can help banks follow know-your-customer rules, spot potential money laundering and more.

Larry Burt

Larry Burt is a Public Cloud Specialist in CDW’s Small Business Technology Solutions Group. More than 10 years with CDW, Larry has helped businesses fulfill a variety of technology needs in the data center, that includes server, storage and virtualization to name a few. In his current role Larry focuses his time on helping customers with understanding their business requirements and fulfilling these with a digital strategy that is focused around the Public Cloud.

Rajiv Jain

Rajiv Jain is a Field CIO/CTO for the Financial Services Vertical at CDW.

Financial services regulations are complex, comprehensive, and constantly evolving. For example, the Sarbanes-Oxley Act of 2002, commonly known as SOX, includes a requirement that banks must ensure that responsibilities for high-risk processes are segregated across more than one employee. Failure to do so could lead to audits, sanctions or fines.

Artificial intelligence and machine learning tools can help banks enhance regulatory compliance and better navigate evolving markets. Here’s how.

The Banking Basics of AI and ML

AI and ML are closely related, but they’re not the same. AI tools are designed to mimic some aspect of human intelligence to help solve problems, reduce error rates or improve process speeds. ML algorithms, meanwhile, are models trained using massive data sets to provide correct responses based on available data.

From a banking perspective, an ML algorithm might be trained using large amounts of both legitimate and fraudulent transaction data to help it spot the differences. An AI tool built on this algorithm could help banks pinpoint potential problems and suggest appropriate responses.

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Language, Learning and Liability: The AI/ML Trifecta

Working together, AI and ML tools offer banks regulatory benefits in three key areas: language, learning and liability.

Using what are known as natural language processing (NLP) and large language models (LLMs), banks can uncover common patterns in fraudulent transaction requests. In-depth analysis of fraudulent transaction data, meanwhile, can help financial organizations build robust learning models for ML algorithms capable of finding problems as early as possible.

Finally, the use of AI solutions to quickly contrast and compare large volumes of current and historical financial data can help banks reduce potential liability by meeting due diligence requirements.

Core Ways that AI/ML Can Help Financial Firms

It’s one thing to talk about potential AI/ML uses and another for banks to integrate these tools across everyday workflows. In practice, AI and ML can help financial firms better navigate compliance concerns in five core ways:

Streamline know-your-customer and anti-money laundering frameworks. In financial services, collecting and analyzing the data required to verify customer identities and identify money laundering risk — known as know-your-customer and anti-money laundering frameworks — are integral parts of regulatory compliance. Doing so effectively, however, is both time- and resource-intensive. With generative AI tools, organizations can extract information from both structured and unstructured data sources to create a complete KYC/AML picture.
Enhance exposure analysis. LLMs allow risk analysts to evaluate massive data sets on securities, investments, and other high-value transactions, in turn making it possible to quickly identify possible exposure points.
Increase risk factor detection. Portfolio and trading book activity help drive financial ROI, but they also present revenue risk if vulnerabilities are missed or exploited. LLMs can monitor these actions in real time to detect potential risk factors.
Reduce regulatory gaps. Firms can’t protect what they can’t see. ML algorithms can be trained to evaluate bank networks for possible regulatory gaps, while AI tools can suggest steps for remediation.
Improve response to changing regulations. Banking regulations are constantly evolving. Consider the recent advancement of Basel III “endgame” proposals, which would require large banks to increase the amount of on-hand capital they hold, use standardized risk models for risk assessment, and account for unrealized gains and losses. NLP and LLM tools can help banks quickly analyze new requirements to ensure nothing is missed and that required actions are summarized.

UP NEXT: Learn three ways AI is helping financial services companies improve security.

Emerging regulatory standards and evolving fraud methods require a rapid response from banks to reduce risk and limit liability. AI and ML tools can help financial firms address internal risks, detect potential fraud, and stay ahead of changing compliance expectations.

This article is part of BizTech's EquITy blog series. Please join the discussion on X (formerly Twitter) by using the #FinanceTech hashtag.