The legacy anti-money laundering (AML) programs commonly implemented by banks are often run on static, rules-based systems. This typically doesn’t involve much more than tagging noteworthy cases one by one, then submitting the compiled data to a separate investigator for final analysis. But not only is this process long, tedious, and draining on the compliance team’s general productivity. When data gathering alone can take days or even weeks, the result is a low real-time detection rate for legitimate threats—which, as banks know, is dangerous to both their assets and their reputation.
But is there a better alternative to conventional case-by-case processing? Luckily for chief compliance officers and other staff who are involved in their home bank’s AML program, the answer is yes. If you’re a stakeholder in your bank’s AML program, you now have the option to explore more high-tech AML solutions that deploy graph analytics. Your new toolkit may require considerable investment and some key changes to the way you run your AML program, notably in the area of compliance. But for the insight, clarity, and protection it will afford you, the switch to a graph analytics-based solution may be well worth it.
Here’s how graph analytics can serve as your next big weapon to fight off financial criminals. Secure your system from threats of money laundering and other nefarious activities, and maintain a clean slate with your AML regulators and your customers.
Why Banks Need to Go Beyond Rules-Based AML Solutions
The simplest reason to consider changing tack in your AML strategy is this: money launderers, terrorist backers, and other financial criminals have become alarmingly good at covering their tracks. Their own methods include:
- Disguising their transactions to be virtually indistinguishable from those of other customers;
- Minimizing transactions with the same banking staff so as not to rouse suspicion, and;
- Moving money through “smurf” accounts, or accounts opened by people with clean records. At the outset, these front men will sport clean criminal records and won’t immediately appear to be connected to money laundering networks or terrorist syndicates.
Tactics such as these are both scary and frustrating for banks that lag behind on AML technologies. Insufficient AML systems make banks easy prey to well-established and technologically savvy criminal syndicates. In addition, these deficiencies will affect a bank’s standing and performance with its regulators, many of whom have levied steep penalties on institutions with slow and outdated AML programs.
Though it may seem costly to upgrade, it’s definitely more preferable to bleeding cash and being vulnerable to malicious agents at the same time. Compliance teams will do well to slowly shift from a linear, case-to-case approach to one that depends on real-time, three-dimensional analytics. And this is where graph analytics solutions come in.
What Graph Analytics Can Do for Your Bank’s AML Program
When the rules have been in place for so long, the most enterprising financial criminals may have already learned the many ways to skirt them. It’s time for even the smaller banks to invest in innovative new systems that can stump criminals and their large web of associates.
This is exactly what graph analytics will achieve. Graph analytics will illustrate, in a highly visual manner, the potential relationships that entities have with each other. In a nutshell, a graph analytics-based solution will utilize the power of certain types of data to uncover complete pictures of customer behavior.
A graph analytics-based solution will use data points or nodes to represent individual customers, and edges to represent potential relationships they have with each other. These relationships can be based on factors like customers’ IP addresses, their country of origin, or the eerily consistent amount of money that they move from their account on a regular basis. The solution can streamline data found from data lakes, disparate files using programs like Microsoft Excel, and third-party applications like those that track international media. A critical difference between a graph analytics AML solution and one that relies on case-by-case tracking is the inability of the latter to expose complex webs of behavior, as well as real-time spikes during certain periods.
After the graphs are generated and analyzed, the bank can use the technology to conduct further research such as whether the data ties back to a list of politically exposed persons or sanctioned entities abroad. Both the bank’s compliance team and its partner investigator can catch trends as they happen, and they can act quickly if there’s significant evidence of criminal behavior. This will cast the bank in a good light when it’s time for regulatory reporting, as regulators will see for themselves that the bank’s AML program is working.
In summary, graph analytics can help a bank like yours detect the threats that may be lurking within your system. It will allow you to track down financial criminals using up-to-date data and, more importantly, a system that works with visual and real-time logic.
For sure, you wouldn’t want to be stuck in a situation where the criminals are more flexible than your own AML staff and system. To avoid that, deprive the money launderers, terrorist backers, and other malicious agents of the luxury of being invisible. Expose incriminating patterns of behavior through an AML solution that utilizes graph analytics, and remain more than one step ahead of the game.