AI-Based False Alarm Identification: A Game Changer for AML Analysts
AML risk-based approach and AML automation
AML analysts focus on many tasks throughout the day with their main responsibility being to monitor and detect suspicious transactions in an effort to prevent money laundering. They spend much of their time investigating cases highlighted by a transaction monitoring system. By identifying false positives at earlier stages, these analysts can focus on more important tasks. False positives occur when a system misidentifies an innocuous transaction as suspicions and flags it for review. Modern financial infrastructure is much more complex with new products, technologies, and processes which must all be taken into consideration. As financial systems increase in complexity, so too must the systems in place for combating financial crime. In this article we will go through the benefits of an AML risk-based approach and explore how anti-money laundering automation can be used effectively. This risk-based approach to AML combined with the automation of AML software can truly be a game-changer for both financial institutions and their AML professionals.
Alternative to risk-based approach to AML
The alternative to a modern, risk-based approach to AML is the traditional method that is currently used by many financial institutions for detecting money laundering. This is a rule-based system that is heavily manual and requires a great deal of attention from analysts. At the beginning of the process, there is a repository that contains information about clients, their transactions, and other pertinent activities. Pre-defined conditions such as transactions above a certain amount, transactions connected to a specific country, cumulative amounts of money sent or received to a counterpart, and other such anomalies have traditionally generated alerts. There are a lot of rules that are applied during this process and, after transactions are singled out, they undergo a manual review where the AML analyst makes the final decision. In addition to being heavily manual, criminals can eventually learn the rules of this process and figure out how to exploit the system. In order to reduce opportunities for criminals, financial institutions must move away from the manual, rule-based approach and implement a risk-based approach to AML, powered by AML automation, which has proven to be effective in combating financial crime. Countries, appropriate authorities, and banks can utilize an AML risk-based approach to better assess and understand their exposure to money laundering and terrorist financing. They can then take appropriate mitigation measures according to the level of risk.
Anti-Money Laundering Automation
How can we recognize which of the alerts warrant further investigation and reduce an analyst’s time spent on false alarms? The solution to this question is simple: utilize an AML risk-based approach enabled by machine learning to perform a risk analysis for each individual case.
A system utilizing a risk-based approach to AML assigns risk scores to particular cases and creates a ranking which can be used as a starting point for a queue of alerts. This in turn, helps limit the number of irrelevant cases by discarding cases below a specific threshold. Discarded cases will not be escalated or further investigated unless other suspicious activities are detected. The AML risk-based approach limits the overall number of false alarms and has numerous benefits including faster escalations to FIUs, less successful money laundering attempts, and better staff allocations. Standardization of data leads to more certainty thereby making communication about risk quicker and easier. Ultimately, the risk-based approach to AML helps with the overall effective management of risk.
By using an AI approach, which enables the automation of AML software, analysts can focus on other important tasks including: working with compliance teams on specific requirements, reviewing data to ensure AML regulations are met, having meetings with regulators, examiners, and auditors about their strategies for monitoring and prioritizing risks, assisting with changes in regulations, and highlighting the implications of new products and services. AML automation can boost AML delivery standards, processing times, and improve overall productivity.
The automation of AML software along with the risk-based approach, when used in conjunction with each other, can detect money laundering at earlier stages and help AML analysts work more efficiently. This can reduce their workload, allow them to focus on other tasks, and give them the ability to work more effectively.
Where We Can Help
Money laundering is a serious problem for financial institutions and economies around the world. Financial institutions with weak AML infrastructure get taken advantage of by criminals and terrorists. This presents a number of significant challenges including proper regulatory compliance, maintaining financial security, preserving an institution’s reputation, and avoiding operational risks like lawsuits. Many of these challenges can be alleviated through the automation of AML software and by adopting a risk-based approach.
Many believe that these challenges are compounded by the insufficient anti-money laundering automation controls within financial institutions. Money laundering has become a serious issue worldwide and has been receiving a lot of attention from national governments and international bodies such as the United Nations, the International Monetary Fund, and the World Bank. As mentioned, the risk-based approach to AML helps overall with the current management of risk.
Detecting instances of money laundering by analyzing the transactions moving through banks and financial institutions has not yet reached a level where AML automation tools can be used effectively. This is where there is significant opportunity for an AML risk-based approach and increased automation of AML software which is specifically designed to incorporate AI to prevent and combat money laundering across the globe. As AML analysts have many critical daily duties, new technologies can offer a way to reduce the overall time that they spend detecting money laundering cases during earlier stages and better utilize their time on human-centric tasks which are critical to their role in combating financial crime.
To conclude, the AML risk-based approach and the automation of AML can be extremely beneficial for financial institutions. Many of the significant challenges that analysts face on a daily basis can be reduced when incorporating an AI approach delivered by anti-money laundering automation. As these new technologies emerge AML analysts can now work more efficiently and effectively by reducing their workload and focus more on higher priority tasks.
Sanah Hamad, Business Development Manager, Comarch