As criminal methodologies are growing more advanced, the fight against money laundering is becoming a massive challenge for all financial institutions worldwide. Therefore, it becomes necessary to put in AML (Anti-Money Laundering) measures. As AML requires dealing with a vast amount of customer data, they are turning to AI and Machine Learning to help them identify and detect money laundering activities.
AI performs AML tasks faster than a human employee, and also, through machine learning, it possesses the capability to modify new threats and detect new money laundering methods. It ensures that financial institutions can adjust quickly to different regulatory environments.
When transaction data of a customer is incorporated into an AML program, AI and machine learning models analyze the behavior to make predictions and perceptions about that customer in the future.
How are AI and Machine Learning advantageous in fighting financial criminals and money launderers?
Customer Perceptions
AI systems enable the CDD (Customer Due Diligence) and KYC (Know Your Customer) systems to occur faster and with greater depth and reach. The AI-based CDD and KYC processes enable the financial institution to
Efficiently identify and collect data from a more excellent range of external sources, including watch lists, sanction lists, and create a factual profile of the customer.
Recognize valuable owners of customer entities by using external data faster and more efficiently.
Accumulate and reconcile customer data across internal systems to remove replication and errors and intensify the density of AML measures among customers.
Automatically enhance dubious activity reports with appropriate data from customer risk profiles or data from external sources.
Unstructured Data
There are other essential steps beyond creating customer risk profiles. As a part of monitoring transactions, screening PEP, screening sanctions, and monitoring media, the AML process requires identifying and analyze the unstructured data. Every financial institution must make an effort to use the unstructured data to recognize their professional, social and political lives by inspecting a range of external sources, which includes public archives, media, social networks, etc... In such circumstances, AI helps the institution recognize those unstructured data. Once the data is collected and analyzed, AI helps the institution prioritize and categorize information to assist risk management.
Reporting Dubious Activity
AI can assist in reporting suspicious activity by producing reports and automatically filling them with accurate information. After their submission of messages to the authority, SARs goes through a process of internal reporting. AI technology can make the SAR process easy as algorithms can generate automated reports with accurate data and transmute that data into an accessible, standardized language to eliminate bureaucratic friction. Because of standardized language and terminology, AI increases an institution’s AML reporting speed and efficiency.
Noise Minimization
The AML system is complex and is a time-consuming procedure; therefore, it is an advantage to incorporate AI within an AML system which helps in adding speed and efficiency. But one of the major hindrances in the process is the level of noise or false positives, which results from incomplete or inadequate data or over-sensitivity of AML steps. In such cases, AI systems play an essential role by generating a significant transformative effect to the noise level generated during the AML process. AI assists the institution in producing higher insight into customers’ transaction patterns and enables them to remove wrong and invalid alerts, making the process costly for the institutions and inconvenient for customers. By minimizing noise, AI and machine learning tools enable AML employees to prioritize and direct the most required money laundering alerts. By doing so, AI more effectively contributes to the fight against financial crime.
Limitations of AI
To keep pace with the increasing risk of financial criminals and money launderers and the need to react faster to those new threats, often new AI and machine learning models are prematurely dashed into the market without proper training. This creates a massive skepticism around AI and Machine Learning technologies. Therefore, banks must remember that AI experimentation comes with diminishing returns. They should focus on performing strategic, production-ready AI micro-projects parallel with human teams to deliver actionable insights and value.
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