The New AML Frontier: AI and ML in the Regulatory Landscape



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The New AML Frontier: AI and ML in the Regulatory Landscape

When it comes to the financial sector, meeting anti-money laundering obligations is one big challenge for enterprises. Investing huge sums of revenue to maintain compliance teams and managing processes is not everyone’s cup of tea. That being said, corporate organisations are moving towards competitive and automated solutions to not only win the market race but also to cut short complex operations and reduce the overall burden. The advent of competitive technologies like Artificial Intelligence, and Machine Learning has given rise to state-of-the-art solutions to combat financial crime.

Why is Increased Adoption a Challenge?

Given the promising potential of AI and ML, they have not seen much adoption in anti-money laundering solutions. There are two main reasons for this are:

  • Most businesses are unaware of how their compliance programs can greatly benefit in the long run using these intelligent solution
  • Oftentimes, compliance officers believe that artificial intelligence is rather a ‘black box’ whose operations are unknown to them; therefore, they are not always sure about how to assess the risks and outcomes

Regulatory authorities direct compliance officers to take into account not only the outputs but also the entire infrastructure of the AML program. Despite all these concerns, businesses are recognizing the importance of digitally automated solutions to address user-centric and enterprise-level problems in the long run. When it comes to optimizing operational workflows and offering seamless services, banking on technology trends seems a win-win scenario for both the corporate sector and customers alike.

Machine Learning — The Dawn of Self-reliant Monitoring

Machine Learning offers two potentially viable solutions for businesses when it comes to evaluating customer risk:

  • Monitoring any suspicious activity on the user’s end
  • Keeping a check on high-risk transactions that have a decent likelihood of leading to financial crime

What a transaction monitoring system does is generate a reasonable number of alert notifications that are triggered as a result of certain red flags. Compliance teams are assigned the task of cross-checking each of these requests against pre-defined parameters and verifying the actual level of risk involved. This is at times tedious and not effective in terms of resource planning, confining enterprises to focus on revenue-building tasks.

What Does ML Have to Offer?

On the other hand, ML-based models, when incorporated in AML compliance solutions, can classify customer behaviour, categorize the level of risk: high, medium, low, and generate suspicious activity reports (SARs) without the slightest hint of human intervention. Alert classifications can now be handled by fully automated solutions that can decide on their own whether a transaction is a regular one, suspicious, or a money laundering instance, ultimately closing alerts based on self-made decisions.

Below are some aspects that highlight why ML is a potential contender in AML compliance solutions:

Dependency: Organizations employing AI in their routine tasks need not invest in a human workforce since self-reliant systems can take autonomous decisions and improve the turnaround time.

Decision-making: Intelligent solutions reduce the effort to invest essential time on repetitive tasks and allow us to focus on complex tasks that need attention. The fact that AI and humans working together creates better opportunities for making informed decisions.

Data Sharing: With an effective Anti Money Laundering mechanism in place, financial institutions can play their role in curbing monetary crime by sharing relevant and timely information through self-generated reports with regulatory bodies such as the FinCEN, or FATF, etc.

The Next Generation of AML Transaction Monitoring

Implementing artificial intelligence in existing ongoing monitoring systems requires fine know-how of how compliance programs work. In this regard, here are some key aspects that a business needs to take into account:

Information acquired from the user at the time of Know Your Customer (KYC) verification and data available as a result of real-time transactions should be accurate and error-free. Most of this acquired data is either riddled with errors or the information provided on the transaction page is incomplete. Enterprises need to address these data-related concerns to streamline their AML compliance programs.

Despite these challenges, financial businesses are increasingly adopting Robotic Process Automation (RPA) that are backed with AI-based models. Even though RPA and AI can exist independently, combining both can offer new innovation to the table in terms of both accuracy and seamless operations. To sum things up, the inclusion of AI and ML have far-reaching benefits for businesses to meet AML compliance in light of global regulations much more effectively.

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