Aggregated Data Fields in Beam
Rules are at the heart of any anti-money laundering (AML) solution. They determine whether a transaction, account, or party should be flagged for suspicious activity. You can see a list of 10 sample AML compliance rules in this blog post.
Traditionally, rules look for linear static criteria. One rule might look for transactions of a certain type that are greater than a specified threshold. Another might look for a customer with a certain occupation living in a specified location. While these rules are effective at identifying individual transactions or customers, they cannot identify patterns and trends and therefore can’t identify deviations from “normal” behaviors. For example, you will want to be able to calculate how many transactions a particular customer typically does in a 30-day period or the average transaction amount for a certain type of merchant.
That’s why Beam provides a highly configurable and dynamic aggregation framework that allows you to generate various types of aggregated data fields to build sophisticated rules. These data fields are calculated in real-time during rule processing, so they reflect true, derived information about customers and transactions. For example, instead of custom implementing a calculation for the buyer's average transaction price in the last 60 days, you can simply add this aggregated data field to your rule: BUYER_AVG_VALUE_OF_TOTAL_PRICE_USD_TRNS_LAST_60_DAYS
Aggregations allow you to identify patterns and trends and then discover deviations from those norms. For example, if a customer typically does 15 transactions per month, but they did 50 transactions this month, you can identify this with a rule that looks for NUMBER_OF_TRNS_LAST_30_DAYS > AVG_MONTHLY_NUMBER_OF_TRNS. Identifying these spikes in activity (often referred to as velocity) is an important component of a comprehensive AML compliance program.
Here are two other examples of aggregated data fields that are useful in rules, supporting the concept of ranges and binning:
A party-monitoring data field that calculates the sum of the cash and quasi-cash (e.g., gift card) transactions greater than or equal to $1 and less than $10,000 in the last 14 days for a given party.
A transaction-monitoring data field that calculates the sum of outgoing cash transactions greater than $10,000 in the last 7 days for the party that executed the linked (reference) transaction that is associated with the given transaction.
Beam provides many commonly required aggregated data fields by default, which you can tune for your needs and policies. You can also create new aggregated data fields, using the defaults as templates or defining new ones from scratch. Aggregations can be scoped (such as by account) and filtered by specific fields (such as category). You can specify units such as days or minutes to define various types of lookback periods, and multi-currency is supported.
To learn more about how Beam’s rules and aggregated data fields can transform compliance in your organization, request a demo.