We’ve got some bad news for financial institutions…
Money laundering is not going to disappear in the digital age.
In fact, it’s only going to get worse.
Think about what we’ve already seen pop up in this space over the last few years:
- Criminals have used digital financial systems to move illicit funds in greater volume, with increased sophistication, in novel ways.
- In response, Anti-Money Laundering (AML) regulations have expanded in both their scope and in the severity of penalties they impose.
- Many financial institutions have tried to fight back by scaling their existing AML approach… with not-quite-sufficient results.
We won’t mince words here— this is a bad situation. And it’s only going to get worse unless you become more agile in your AML approach.
Why You Can’t Just Scale Your Legacy AML Approach
Before we get too deep into the weeds, we want to take a quick moment to make one point clear— there was nothing inherently wrong with legacy AML approaches and they seemed to have worked well in prior environments. They have simply run their course and break down in the digital age for a few core reasons, and expanding on a legacy approach is costly and less effective than alternatives.
First, legacy approaches can only detect known types of money laundering transactions. They document the “rules” of known fraudulent transactions, and then search for these rules from every transaction processed through their network. But modern criminals create new types of money laundering transactions every day that bypass known rules, and evade detection.
Second, legacy approaches can only handle a low volume of money laundering transactions. Every time they detect a potentially fraudulent transaction, they raise an alert and as more data sources are introduced, there is a potential for even more alerts. Digital systems are able to process a much higher volume of transactions—both legitimate and fraudulent—than ever before, and are flooding financial institutions with alerts. Often more than 95% of these alerts are false positives, but they still must be manually investigated and acted upon, one-at-a-time, by multiple compliance officers.
Third, legacy approaches are highly rigid and linear, and ignore the basic principles of prioritization and efficiency. They produce the same false alerts day-after-day. They force compliance staff to follow the same set of steps for every alert that’s raised, regardless of each potential threat’s actual risk level.
These three problems combine to create a vicious cycle in the digital age.
- To detect new AML threats, you have to keep adding new rules to your detection processes…
- But as you add new rules to your detection processes, your system triggers more and more alerts, most of which remain false positives…
- To process all these new alerts, you bring in more and more staff members to investigate all of these alerts…
- But no matter how many new systems and staff members you onboard, bad actors and billions of dollars of illicit funds still slip through…
- And despite your good intentions, you continuously break AML compliance, and open yourself to escalating fines and penalties.
In short: Legacy approaches to AML compliance just do not work in the digital age. You will never be able to scale your way past this fundamental truth. More of the same will get you more of the same. You need a completely new approach.
How to Achieve Money Laundering Compliance in the Digital Age
Yes, you must turn your conventional approach to AML on its head.
Going through this kind of total process re-design isn’t some foreign concept to financial institutions like yours.
You are already in the middle of a digital transformation. You have already created new, automated, data-driven, “smart” processes to revolutionize how you perform basic day-to-day tasks like booking loans and opening new accounts. And now it’s just time to apply these Agile principles and tools to your AML activities.
Here’s what that looks like:
- You will replace traditional rules-based detection processes with AI-driven detection processes that can uncover both known and unknown threats.
- You will reduce alert volume by leveraging Machine Learning techniques and models that accurately weed out false positives and leave a smaller pool to investigate.
- You will increase the efficiency of your investigations by automating as many actions as possible, and by routing what remains to compliance officers based on prioritized risk levels.
Bringing Agile AML to Your Organization: The Fastest, Easiest Path
If you can pull off this transformation of your AML efforts, you will be richly rewarded. You will create:
- Reduced alert volumes and false positive rates.
- Faster, more accurate AML detection & response.
- Efficient, automated, “always on” AML capabilities.
- Modern, streamlined AML staffing models.
At LoBue, we have made Agile AML a core. We have done our best to outline a practical approach to Agile AML in this article—one that makes it clear how your existing AML activities must evolve to meet the high-stakes demands of the digital age.
However, if you seek a quick path and a methodical approach to bringing Agile AML to your organization, then we would be happy to set up a no-obligation assessment of your client on-boarding and AML process and systems.