As criminal methodologies are growing more advanced, the fight against money laundering is becoming a huge challenge for all the financial institutions around the world. Therefore, it becomes necessary to put in AML (Anti-Money Laundering) measures. As AML requires to deal with a huge 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 are able to 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?
AI systems enable the CDD (Customer Due Diligence) and KYC (Know Your Customer) systems, to take place at a faster rate and with greater deepness and reach.
Efficiently identify and collect data from a greater range of external sources which include 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.
There are other important 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 to 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 the reporting of doubtful activity by producing reports and also, by automatically filling them with accurate information. After their submission of reports 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 in order to eliminate bureaucratic friction. Because of standardized language and terminology, AI increases the speed and efficiency of an institution’s AML reporting.
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 is the result of incomplete or inadequate data or over-sensitivity of AML steps. In such cases, AI systems play an important role by generating a significant transformative effect to the level of noise generated during the AML process.
AI assists the institution to produce higher insight into customer’s transaction patterns and enables them to remove wrong and invalid alerts which makes the process costly for the institutions and inconvenient for customers. By minimizing noise, AI and machine learning tools enable AML employees to better 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
In order 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 huge 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 in parallel with human teams to deliver actionable insights and value.