Unlock the full potential of your Anti-Money Laundering (AML) analysts using Artificial Intelligence (AI)-based solutions.
In their day-to-day risk assessment operations, Anti-Money Laundering (AML) analysts spend much of their time dedicated to routine and repetitive tasks. According to a study published by the research firm “Celent” (Source: Ray, Arin. 2018. The Dawn of a New Era in AML Technology), banks report that between 60 and 80% of analysts’ time is spent on low level data management.
In this article, we’ll highlight these activities in three main points and show how AI can support by automating non-value-added tasks, providing actionable insights, and engaging with AML analysts.
1. Gathering data, documents and information.
For most AML activities (whether for KYC verification, due diligence, customer onboarding or monitoring processes), an analyst will typically spend more time researching, collecting and gathering data/information about the subject, than performing analysis in order to make the right decision.
Artificial intelligence (AI) can support this need by automating business processes using Robotic Process Automation (RPA). It is estimated that by the end of 2021, RPA will eliminate 40% of non-value added tasks in the finance office. This would leave enough time for analysts to focus on a very important aspect of their job, ensure all AML requirements are strictly followed to prevent any money laundering activity.
2. Reviewing AML alerts and sorting out “true positives” from “false positives”.
Existing rules-based anti-money laundering systems are characterized by a high rate of “false positives” (rate of 95 to 99%) because they are not always able to take into account complex interdependencies between the different activities carried out to launder money.
The integration of solutions based on artificial intelligence (AI) and machine learning (ML) could dramatically reverse these high ratios by leveraging the latest supervised and unsupervised learning techniques to uncover anomalies and improve the reach of detection. This will give AML analysts sufficient time to focus and properly process the “true positives” worthy of being labeled “suspect”
3. Dealing with changes and increased regulatory oversight.
One of the biggest AML challenges for financial institutions is dealing with ever-growing customer due diligence requirements, the difficulty of managing cross-border and multi-jurisdictional compliance requirements. Hence the need to find qualified resources with in-depth knowledge of AML.
The ability of Artificial Intelligence to compile and process data with precision can interpret compliance documents to provide insights to AML analysts. AI applications can even help in maintaining compliance by constantly checking sources of compliance rules and notifying AML analysts about any changes in regulations.
The human factor remains irreplaceable in the fight against money laundering. The expertise and knowledge of AML analysts will always be necessary to assess the potential risk of money laundering. Applying artificial intelligence and machine learning to finance operations can be a great ally in empowering analysts in their day-to-day operations.
AI is one of the most popular emerging technologies that will help tremendously and effectively in the fight against financial crime.
