Advise #7: Replace TM scenarios with intelligent methods of identifying financial crime risks

Even though banks have been working with optimising their TM scenarios for many years, false positive ratios on TM alerts are often way above 90% and that results in a lot of waste.

The reason is the (too) simplistic nature of TM. There are many good reasons to transact with high-risk countries or to deposit a decent amount of cash, and therefore the TM Operations unit still ends up looking for the few needles in the haystack representing the suspicious behaviour.

The simplistic nature of TM scenarios also results in more complex criminal patterns remaining undetected, so the answer seems to be quite straight forward – upgrade the financial crime defence by using the more complex analytical capabilities like network- and payment content analysis.

These tools have already been embraced by larger banks, but most often as something on top of the existing defence, not as a replacement. I believe the entire industry should be brave enough to transform the defence, even if that leads to some simple suspicious behaviour remains undetected.

Some TM scenarios are simply not representing a proportionate effort – especially considering that the authorities don’t have the strength to investigate all the “small stuff”.

The intelligent tools can identify the complex crimes that also are the most severe. These should be prioritised end to end, e.g. from identification in a bank to a conviction in court.

Without going into network and payment content solutions in details, I will still provide you with a bit of insight on why they work much better.

Banks have already identified thousands of SAR’s. When someone is already identified as suspicious (or e.g. proven criminal through adverse media), it is likely that someone connected to them is also demonstrating suspicious behaviour.  

Another area is to conduct theme-based analysis. One example is to define patterns that can indicate if trafficking for black labour in the construction industry or prostitution are occurring. Naturally these solutions also result in false positives, but not anywhere near 90% of the time.

One final advice from an often-missed operational excellence point of view: Be very careful with broad access to the network and payment visualisation tools. If they are made available for the entire operations team, they will be used to go down many rabbit holes – also where there is nothing to find. Instead use scores created by the tools and only investigate the highest scores and only done by people that have learned how to use the tools.