Smarsh is a Business Reporter client.
Financial services organizations are increasingly turning to AI-powered tools to navigate the growing regulatory demand and optimize their risk postures. These tools allow innovators to respond quickly to changes in market conditions.
As the compliance perimeter expands to additional areas such as board governance and third-party risk management, financial institutions must consider AI as a key 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 risks. The increase in scale creates significant opportunities for banks to leverage large amounts of communication data, increasing visibility into their operations and improving their ability to detect problems early.
These discussions have allowed us to focus on three main AI-based compliance use cases that have emerged for large financial firms:
1. Integration of intelligence into legacy systems
Using data analysis and machine learning, compliance teams can drastically reduce the time required to verify false positives and better detect real risks. Organizations leverage these benefits by using AI to access and analyze data from their legacy archives. To do this, companies 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, information security, and potentially cybersecurity teams.
2. Communication surveillance to detect market misconduct
Regulators around the world require financial services companies to capture their communication data, store and archive that data in accordance with regulatory requirements, and analyze it for misconduct. AI enables scaling, eliminates noise, and strengthens organizations’ efforts to reveal true warning signs.
Traditionally, compliance teams used lexicons to search communications for indicative terms of misconduct. These searches relied on keywords to generate alerts. Although lexicons are still in use at some organizations currently, the volume of alerts generated – and the abundance of false positives found in these alerts – is overwhelming compliance teams. Natural language processing (PNL) enables compliance teams to quickly detect malicious behavior of traders in written or spoken communications, improving the monitoring process.
3. Market surveillance beyond linguistic communications
As technology and regulations evolve, financial organizations must recognize and adapt to an expanding area of risk. Companies 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 practical view of what is happening beyond the company’s recorded communication channels.
The “superficial risk area” goes far beyond text and audio-based digital communications. As a result, these extended areas of market and trade surveillance are becoming an increasingly high priority for banks – and fertile ground for early AI applications.
Our technology-driven world continues to progress and evolve, and the same must happen with financial institutions. With the incorporation of AI and the benefits that result from its adoption, banks can no longer rely on “old methods.” It is 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 with experts from UBS, HSBC, BMO and more.