AI in the Real World: Three Financial Services Use-Cases
Smarsh is a Business Reporter client.
Financial services organizations are increasingly leaning on AI-powered tools to navigate heightened regulatory demand and optimize their risk postures. These tools enable forward thinkers to respond with agility to changing market conditions.
As the compliance perimeter expands to additional areas such as board governance and third-party risk management, financial institutions must consider AI a pivotal element of compliance and risk management strategies.
Discussions with global financial leaders and compliance professionals reinforce the need for AI solutions to scale operations and detect risk. Increased scale creates significant opportunities for banks to leverage vast amounts of communications data, increasing visibility within their operations and enhancing their ability to detect problems early.
These discussions enabled us to zero in on three primary AI-powered compliance use cases that have emerged for large financial enterprises:
1. Integrating intelligence into legacy systems
Using data analytics and machine learning, compliance teams can dramatically reduce the time needed to verify false positives and better detect true risk. Organizations leverage these benefits using AI to access and analyze data from their legacy archives. To do so, firms must understand what data they have available, where that data is stored, and the infrastructure needed to retrieve and analyze it. This requires close collaboration with IT, infosec, and potentially cyber-security, teams.
2. Communications surveillance for market misconduct
Regulators worldwide require financial services firms to capture their communications data, store and archive that data according to regulatory requirements, and analyze it for misconduct. AI enables scale, eliminates noise, and strengthens organizations’ efforts to surface actual red flags.
Traditionally, compliance teams used lexicons to search communications for terms indicative of misconduct. These searches relied on keywords to generate alerts. While lexicons are still in use at some organizations today, the volume of alerts generated—and the abundance of false positives found within those alerts—is overwhelming compliance teams. Natural language processing (NLP) allows compliance teams to swiftly detect malicious trader behavior in written or spoken communications, enhancing the monitoring process.
3. Market surveillance beyond language-based communications
As technology and regulations evolve, financial organizations must recognize and adapt to an expanding surface area of risk. Firms gain a comprehensive view of the broader market, as well as employee activity, by expanding surveillance beyond literal language-based communications. These efforts provide a more holistic and more actionable view of what’s taking place beyond company-registered communication channels.
“Surface area of risk” expands far beyond text- and audio-based digital communications. As a result, these more extensive areas of market and trade surveillance are becoming an increasingly higher priority for banks—and fertile terrain for early AI applications.
Our technology-driven world continues to progress and evolve, and so must financial institutions. With the incorporation of AI and the benefits that result from its adoption, banks can no longer rely on the “old ways”. It’s time for financial institutions to understand AI—and the future of compliance.
Want to learn more? Read our white paper, Banking on the Future of AI-Driven Compliance.
This article originally appeared in Business Reporter.
Main image credit: iStock-1296686780
