AI for Communications Surveillance and Compliance – Two Use Cases

Sharon Moran

Sharon is a former Senior Functional Analyst at a major global consulting firm. She now focuses on the data pre-processing stage of the machine learning pipeline for LLMs. She also has prior experience as a machine learning engineer customizing OCR models for a learning platform in the EdTech space.

AI Communications Surveillance-1x min
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Communications surveillance in financial services involves both monitoring and analyzing electronic communications within a financial institution to prevent fraud, ensure regulatory compliance, identify and manage risks resulting from inappropriate activity, and maintain integrity in financial markets. 

Preventing insider trading has long been a concern in financial markets for many decades. The increase in daily use of technology has increased the volume of communications that companies need to surveil. 

Long-time employees often have more conservative attitudes toward social media use. A 2015 research paper highlighted how younger employees are more likely to view organizational knowledge as a commodity. The same research paper also explained how financial institutions rely on raising their employees’ awareness as an essential contributor to addressing the misuse of social media.

Legacy systems for communications surveillance collect data at the expense of efficiency. The extra data often captures a lot of false positives that add to the workload and reduce the productivity of governance personnel.

More is not necessarily better when it comes to data archiving. While companies are required to comply with regulatory requirements that are not always clearly defined, they also need to strike a balance between meeting those requirements, anticipating how future requirements might evolve, and maintaining efficiency for their governance personnel. Additionally, companies need to navigate expertly, given the potential criticisms from workers’ advocates or even the company’s own employees.

This article will explore two emerging AI use cases in communications surveillance in financial services.

  • Uncover potential compliance risks in employee communications: Using machine learning and refined lexicons to identify potential risks in employee communications across disparate channels.
  • Selectively capturing content to detect regulatory compliance risks: Leveraging deep learning, natural language processing, and optical character recognition to detect regulatory compliance risks in chat content and audio and video recordings.

Use Case #1: Uncover Potential Compliance Risks in Employee Communications

As employers, financial services firms are responsible for capturing and monitoring the digital communications of their employees, not only on company assets but also on personal devices. If companies fail to observe these varied communications, they can run afoul of global regulatory compliance.

In 2022, some of the biggest banks, including Goldman Sachs, Morgan Stanley and Bank of America, were hit with over $2 billion in fines from the Commodities Futures Trading Commission (CFTC) and the Securities and Exchange Commission (SEC).

The CFTC and SEC are just two of the list of regulatory bodies that can levy fines against a financial services company. Other regulatory bodies include:

  • Financial Industry Regulatory Authority (FINRA)
  • Commodities Future Trading Commission (CFTC)
  • Investment Industry Regulatory Organization of Canada (IIROC)
  • Financial Conduct Authority (FCA)
  • Markets in Financial Instruments (MiFID II)
  • National Futures Association (NFA)
  • Securities and Futures Commission (SFC)
  • Monetary Authority of Singapore (MAS)

Headquartered in Portland, Oregon, Smarsh is a SaaS (Software as a Service) company that provides industry-leading archiving, compliance, supervision, and e-discovery solutions for financial services companies and other companies operating in highly regulated industries. (Full disclosure: Smarsh is currently an Emerj client; citation of this use case strictly abides by Emerj’s editorial guidelines. -ed.)

Smarsh Enterprise Conduct

The following video, just over two minutes in length, explains Smarsh’s AI-powered supervision and surveillance system.

According to the case study documentation, Smarsh claims their Enterprise Conduct systems capture and retain all communications across over 100 supported channels, including apps like point-in-time snapshots of entire interactions.

The company also claims the Conduct system helps companies improve compliance productivity. It discreetly monitors multiple forms of conduct and uses machine learning, including natural language processing and refined lexicons, to:

  • Identify evidence of misconduct in regulated communications
  • Surface those instances for review

The documentation further claims Conduct system enables faster, more effective supervision and allows configurable policies. It analyzes a company’s communications using machine learning, natural language processing, and refined lexicons. 

A key benefit the case study also claims is that the system significantly reduces noise in a reviewer’s workflow. Smarsh also claims the system is able to Identify previously hidden risks and alerts a company’s surveillance team to potential policy violations, enabling the company to stay ahead of internal threats.

A customer success story on the Smarsh website shows that CUNA Brokerage Services benefitted from Smarsh’s Professional Archive product. The most significant benefit demonstrated was that employees were able to concentrate on other activities because the product handles indexing, policy checking, and retention of content via automation.

Use Case #2: Selectively Capturing Content to Detect Regulatory Compliance Risks

Remote work during the pandemic resulted in a rise in how reliant companies were on collaboration tools such as Microsoft Teams and Zoom. Staying digitally connected during this time was a necessity for many industries, including financial services.

Theta Lake provides a compliance product suite that the company claims:

  • Performs digital content analysis across a range of product offerings
  • Offers end-to-end capabilities that handle ingestion and analysis all the way through to review and archiving data from Zoom’s phone and video meetings.
  • Can detect both regulatory and corporate compliance risks through Theta Lake ComplianceMD product 

In legacy finance, a transcriptionist would make a written, verbatim, or possibly clean verbatim, transcript of everything that was said during a meeting. However, due to changes in regulatory requirements, Tupicoffs’ purpose for recording these meetings was to maintain compliance. However, they were faced with such an enormous amount of audio and video recordings that they found it difficult to review.

From the case study on Theta Lake’s website, Certified Financial Planner with Tupicoffs Neil Kendall explains: 

“We had all these video and Zoom video and phone recordings, but they required us to spend more than the full duration of the recording to review them thoroughly, including pauses and note-taking. We had the archived recording history, but we didn’t have any overview or review workspace. This made it difficult to assess the recordings, determine how things were going, or if we were recording the wrong things.”

The following video, under a minute in length, explains how Theta Lake analyzes digital content for compliance.

According to the Theta Lake case study, Tupicoffs saved money by using Theta Lake’s Security and Compliance Suite because it eliminated the need for them to hire additional staff. The case documentation also reports that Tupicoffs realized both operational and financial efficiencies. 

Other details covered in the case study indicate that resource cost savings for Tupicoffs were enormous, as they estimated it would have taken three full-time employees to oversee the process manually. Instead, a compliance manager now spends only two hours a week reviewing automatically flagged output from Theta Lake.


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