Machine Learning: Rules vs. Models in Anti-Money Laundering Platforms
Money laundering. Fueled by mobster movies and international espionage thrillers, the phrase has a mysterious, exciting edge to it. But as is often the case, the truth is far less appealing than the glitzy Hollywood version.
Considering the consequences, it’s no wonder governments enact AML regulations. These regulations have honorable and important intentions, but there’s no denying the ever-evolving compliance headaches they create for financial institutions.
What’s more, despite these regulations, money laundering continues to soar — in part because of technology. Per the United Nations on Drugs and Crime, advances in technology and communications have created “a perpetually operating global system in which “megabyte money” (i.e., money in the form of symbols on computer screens) can move anywhere in the world with speed and ease…The estimated amount of money laundered globally in one year is 2 – 5% of global GDP, or $800 billion – $2 trillion in current US dollars.”
What is the solution? How can financial institutions avoid the repercussions of ineffective AML programs and actually help fight financial crime? What’s more, how can banks accomplish this for today, along with the unknown financial crime schemes of the future?
The answer is machine learning.
Why Machine Learning powers AML Today and Tomorrow
Machine learning is the cornerstone of state-of-the-art AML programs today and in the future of money. It creates more efficient and effective teams by automating case enrichment and prioritization for investigators. Automation significantly decreases false positives, which means teams don’t waste time on meaningless alerts. It also allows for more accurate risk scoring. According to ACAMS, “In one instance, a bank reduced the time (taken to work alerts) from several weeks to a few seconds.”
Rules only vs. Rules with Machine Learning Models
Legacy AML systems tend to provide high-volume, low-value alerts because they run on engines that only use rules. The overwhelming amount of false positives a rules-based system creates is akin to crying wolf. Depending on the size of the bank, analysts investigate around 20 – 30 false alerts a day. Unless you have unlimited resources to review alerts, you’ll want a different strategy. Substantial fines — not to mention the media attention that goes with them — have been levied on institutions for failing to devote sufficient resources to review alerts generated by automated AML systems.
AML programs powered by machine learning often utilize both rules and models, not just rules. Using both rules and models dramatically reduces false positives and increases operational efficiency, which, ultimately, helps decrease the chances of getting fined.
To help understand why running rules and models together is so effective, let’s discuss how rules and models-based approaches work.
How rules-based risk engines work
Rules-based risk engines work by using a set of mathematical conditions to determine what decisions to make. It works like this:
If an account shows more than $10,000 in cash transactions in 14 days, then raise an alert
One of the significant pros of using an advanced rules-based engine is that analysts can quickly create and implement new rules. Note that this isn’t true for all ML systems, only the more robust and innovative ones. The advantage is a clear rule with specific calculations, which makes it easier to demonstrate to regulators why and when the system flagged the event as suspicious.
But rules alone aren’t sufficient because they have too many limitations. They’re too dichotomous to understand context and dive deeper than formulas. They also have fixed thresholds, only yes/no scenarios, produce too many false positives, and have trouble detecting relationships between transactions — to name a few.
How machine learning risk engines work
Machine learning for AML strengthens rules with models, which further helps reduce high-volume, low-value alerts. That’s because machine learning models are trained with historical data to predict future behavior. Models work like this:
- Data scientists feed the machine massive amounts of historical data about known money laundering cases.
- The machine uses the data to run statistical models, not deterministic rules.
- The machine learns what money laundering has looked like in the past and, equally important, what normal behavior looks like as well.
- The machine predicts the risk of money laundering based on known money laundering cases or by referencing cases that were reported to the regulator.
One of the benefits machine learning brings to the table is the ability to learn and adapt continuously. However, it’s important to note that machine learning models are only as good as the data you feed them. It’s a bit circular but correct to say: if you can’t provide good labeled data to learn from, the machine can’t learn.
Also, machine learning models take time to, well, learn. That makes them slower to implement, but, once deployed, newer machine learning platforms provide an integrated feedback loop. Essentially, this means the machine learns from new behavior and requires little maintenance to keep up with evolving behaviors in financial crime. So while it takes a bit more time to deploy, machine learning makes up for that time by providing more accurate alerts. Not to mention, ML saves your team countless hours they would have otherwise spent building and adjusting thousands of rules.
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