The Promise of New-Age Technology in Anti-Money Laundering (AML)

anti-money laundering

anti-money laundering

How AI and machine learning can advance anti-money-laundering programs?

Recent technology advancements have made everything easier and much more effective. From performing complex tasks to automating business processes, technology is impacting every aspect of business and life. Financial services are more squeezed by technology breakthroughs as the industry deals with vast datasets and issues related to financial processes. Money laundering is one of the biggest financial crimes that is used to conceal the nature, source and act of a crime and give a legal image. According to the IMF, the money laundering rate is between 2-5 percent of the world’s GDP. This is where anti-money laundering (AML) comes to the rescue.

AML is a set of procedures, regulations and laws that thwart the laundering of money from illicit activities. While AML is a complex issue for financial institutions, advances in artificial intelligence and machine learning enable firms to apply new processes and technologies that aid compliance officers and AML investigators to investigate illegitimate activities in the financial ecosystem.

Artificial intelligence, machine learning and big data have already proved effective solutions for tackling financial crime. Financial services providers today are embracing more technologically focused approaches and flexible and holistic programs to spot and address anomalies more efficiently. AI is capable of identifying patterns of transactions, behaviors and anomalies at a rapid pace, allowing compliance professionals to focus more on analyzing the outcomes, inspecting root causes and integrating their findings with other financial institutions or authorities. On the other side, big data has enabled organizations to outline strings of transactions from just tracking financial crime at a transaction level, enabling connections to be established and patterns to be detected in the data. It helps trace the sources of illegal activities.

Technologies That Can Help Optimize AML Compliance

Machine Learning

Machine learning helps businesses in the analysis of vast amounts of data effectively. As a branch of AI, this technology is effective in anti-money laundering. PwC found that almost 90-95 percent of the alerts generated are false positive. This leads to huge operational overhead for financial institutions as well as the risk of missing genuine alerts. AI and ML solutions can ease this challenge and help banks to improve the quality of alerts. When coupled with hybrid models comprising human resources supported by AI, AML solutions can be developed that provide substantial benefits, such as compliance costs reduction and improvements in the transaction monitoring process leading to greater efficiency.

Robotic Process Automation

Since financial institutions face challenges like heightened regulatory scrutiny and increasing cost pressures that are affecting AML and KYC processes, RPA can ease these challenges. RPA can be used for setting up customer data, collecting, validating and compiling customer information, and customer screening. It can also help in risk assessments, account closure processing and regulatory monitoring and data collection. 

Cognitive Computing

Along with AI/ML and big data, cognitive computing is showing promise in cutting manual activities of anti-money laundering regulations while minimizing risk. It makes it possible to cut the manual efforts of KYC compliance by as much as 50 percent. It also improves banks’ overall ability to lower the risk of money laundering in a near real-time.

Thus, the use of advanced, new-age technologies can improve financial institutions’ ability to improve anti-money laundering efforts. These technologies will also help in the successful implementation of an AML compliance program that meets industry standards and regulatory expectations.