Automated Surveillance Improves Noncompliance Detection
By 2030, AI will save financial institutions $1 trillion. This represents an estimated 22% reduction in operating expenses. These cost savings are possible due to both the improved efficiency and reduced operational costs by the integration of AI into legacy compliance systems. By using advanced surveillance that can capture electronic, visual, and audio data, today’s AI-driven compliance platforms can gather and process these diverse forms of inputs into meaningful compliance-related data more accurately and at a faster rate than humans.
Regional financial institutions like Maybank and DBS are right alongside major banks like HSBC and Standard Chartered in developing their own AI-driven solutions to automate surveillance of the vast amounts of compliance-related information. This is helping streamline compliance detection while also reducing the likelihood any fraud and abuse would go undetected. However, the potential benefits of integrating AI into legacy compliance systems extend far beyond the cost savings and efficiency gains inherent to the automation of surveillance. This is particularly true with respect to the benefits of automation on anti-money laundering compliance programs. However, there are several ways AI can be used to improve the accuracy and expand compliance management capabilities in ways that go far beyond clear AML benefits.
Regulatory Change Management and Reporting
In the global financial industry, compliance managers have to keep track of the hundreds of regulatory changes that occur every day. In the past, this required the dedicated efforts of teams of employees sorting through massive amounts of compliance change data and manually integrating relevant information into legacy compliance tracking systems. However, leveraging the power of AI-driven automation allows today’s regulated financial firms to increase the accuracy and efficacy of these processes for regulatory change management. Top AI compliance platforms are able to eliminate the issues inherent to this inefficient data-silo approach to regulatory change management – particularly with respect to eliminating the millions of false alerts that are bogging down compliance management processes across the industry.
Compliance officers at large financial institutions have been trying to navigate among the sea of red flags being raised by the proliferation of compliance-related data. By effectively automating regulatory compliance and change management processes, AI-driven compliance platforms are eliminating these types of inefficiencies, which is leading to improvements in both regulatory change tracking and timely compliance reporting. Furthermore, the automation of these processes also mitigates the impacts of cognitive bias inherent in human-led investigations. As a result, AI is also improving compliance readiness.
Deterring Rule Breaking and Financial Crime
AI is a key instrument in making compliance departments at financial firms more efficient, but more immediately they help with the process of detecting fraud. For example, AI-supported platforms can identify fraudulent employees who create fake accounts by tracking email and IP addresses and ensuring that multiple accounts are not being run from them.
Furthermore, by using natural language processing and other forms of advanced surveillance methods, AI compliance platforms can target and identify noncompliant behavior in real-time through automated review of emails, voicemails, messages, and other data. Modern AI-powered compliance platforms can both collect this diverse data automatically and develop a sophisticated analysis of compliance-related data. This generates high-quality metadata, which improves the accuracy of further investigation.
Intelligent natural language processing can effectively curb market abuse by detecting malicious intention within trader text or voice chats in a manner that’s analogous to looking for a needle in a haystack. AI-driven compliance platforms are capable of processing millions of chats across various data types, which is often where fraud and abuse starts. For example, this software can flag a scenario in which one trader offers another a hot tip about a new investment and then suggests the two of them take their discussions offline.
By improving the quality of compliance investigations, AI can even be used to prevent criminal activity and monitor potential threats to customers and employees. Banks – including Nordea – that have been caught up in money laundering enforcement actions are now facing penalties that could have been avoided with the more effective use of AI-powered technologies like automation and natural language processing.
When Applied Appropriately, AI Can Be a Catch-All Solution
AI compliance technology is already capable of reading and performing tasks such as extracting metadata, identifying entities that are referred to, and understanding the intent of every regulated activity undertaken at a global financial institution.
Automated compliance processes made possible through the integration of AI with legacy compliance tracking systems also frees up valuable human resources for improved strategic thinking. The savings in cost, time, and energy made possible by these insights allow financial institutions to put human power and resources towards other aspects of the business. While it will not replace the teams that staff compliance departments at global banks currently, AI has proven its worth and will continue to play a large role in financial institutions as we get closer and closer to fully-autonomous compliance.