Smarsh is a Business Reporter client.
Financial services organizations are increasingly relying on AI-powered tools to address increased regulatory demand and optimize their risk postures. These tools enable forward thinkers to respond nimbly to changing market conditions.
As the compliance perimeter expands into additional areas such as board governance and third-party risk management, financial institutions should consider AI as a critical element of compliance and risk management strategies.
Discussions with global financial leaders and compliance professionals reinforce the need for artificial intelligence solutions to scale operations and detect risks. Greater scale creates significant opportunities for banks to leverage large amounts of communications data, increasing visibility within their operations and improving their ability to detect problems early.
These discussions allowed us to focus on three main AI-powered compliance use cases that have emerged for large financial companies:
1. Integrate intelligence into legacy systems
By using data analytics and machine learning, compliance teams can dramatically reduce the time needed to verify false positives and better detect real risk. Organizations take advantage of these benefits by using AI to access and analyze data from their legacy files. To do so, companies must understand what data they have available, where it is stored, and the infrastructure required to retrieve and analyze it. This requires close collaboration with IT, information security and potentially cybersecurity teams.
2. Monitoring of communications for market misconduct
Regulators around the world require financial services companies to capture their communications data, store and archive it in accordance with regulatory requirements, and analyze it for misconduct. AI enables scale, cuts through the noise, and strengthens organizations' efforts to detect real red flags.
Traditionally, compliance teams used lexicons to search communications for terms indicative of misconduct. These searches were based on keywords to generate alerts. While lexicons are still used in some organizations today, the volume of alerts generated (and the abundance of false positives found in those alerts) is overwhelming compliance teams. Natural language processing (NLP) allows compliance teams to quickly detect malicious business behavior in written or spoken communications, improving the monitoring process.
3. Market surveillance beyond language-based communications
As technology and regulations evolve, financial organizations must recognize and adapt to an expanding risk surface. 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 registered communication channels.
The “risk surface” expands far beyond text- and audio-based digital communications. As a result, these broader areas of trading and market surveillance are becoming an increasing priority for banks and fertile ground for early AI applications.
Our technology-driven world continues to progress and evolve, and so do financial institutions. With the introduction of AI and the benefits that come 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 whitepaper, Banking in the Future of AI-Powered Compliance with experts from UBS, HSBC, BMO and more.
