Let AML monitoring find the FATCA needles in your haystack
FATCA and the Model IGAs define certain thresholds that determine the relevance of new accounts and the level (and timing) of scrutiny required for pre-existing accounts. For example, under IGA, a new individual customer is not relevant until and unless
the aggregated balance of their depository accounts exceeds $50,000 on the last day of a given calendar year, beginning with 31 December 2014. Only when that threshold is met do you
need to ask a customer for self-certification, and confirm the reasonableness of that self-certification against information collected at account opening for purposes like… AML!
So why bother all new individual customers as of 1 January 2014 with a demand to certify that they are not a U.S. taxpayer? Besides annoying local customers who have never set foot in the United States and for whom $50,000 in deposits may be a distant
goal, it also maximizes the number of verification cycles required to “confirm the reasonableness” of someone’s self-certification against AML and other onboarding information.
A system with an AML monitoring heritage should be able to easily select and alert customers who fit the definition once a year, which means that you can minimize the work you need to do and maximize the time you have to do it. If a system can aggregate
end-of-year balances across accounts opened by a client after a certain date, you can simply end up with a list of people every January 1st from which you need to obtain self-certification in the next 90 days. How you then execute that process depends on the
size of the list and how you are organized. Options range from the FATCA team picking up the phone and mailing out letters, to an online banking system taking the list and pushing out digital self-certification forms.
That said… we do also see institutions choose to take the pain straight away and make self-certification a standard part of their onboarding Know-Your-Customer (KYC) process. The rationale being that once a customer has gone through account opening, getting
them to respond to this sort of request at some point in the future may be more difficult.
Either way, a detection system can further reduce your work by automatically checking each customer’s self-certification claim against the relevant AML data it received, such as address, phone number and place of birth. The system provides a “stamp for approval”
if no contradictions are found, or issues an alert and/or triggers a remediation workflow if something is wrong or missing.
Generating alerts for customers who meet certain criteria is something that comes naturally to an AML monitoring system, which should then also give you tools and processes for working through such lists based on predefined procedures. And FATCA has other
uses for those capabilities.
For example, applying the regulations’ thresholds can reduce the number of pre-existing accounts for which the institution needs to search electronic and paper records for U.S. indicia. Similarly, it can pre-select only those entity, account and payment
types relevant to the regulation. Further, you must also consider how you can prevent unnecessary work for customers who went through remediation or self-certification last year. It’s this kind of slicing-and-dicing that AML monitoring systems have been doing
for years, so leveraging that capability for FATCA is both effective and efficient.
Next week, part 3 looks at how AML systems can help you demonstrate your FATCA compliance to the regulators.