Top RPA use cases for Anti Money Laundering (AML) compliance

by Rajesh Agarwal, on Oct 13, 2020 12:17:11 PM

As the post COVID world rushes full gear into opening trade and business transactions across the globe, there is a lot to gain and lose. One of the prime issues in the post COVID world is the close monitoring of money laundering and fraud activities that usually take place under the garb of initiating trade. Technologies, such as Robotic Process Automation (RPA) and Artificial Intelligence / Machine Learning (AI/ML), are the primary measures for sieving the wheat from the chaff. Anti Money Laundering (AML) compliance measures have to be deeply integrated in banking and trade finance process, such that the bad actors are continuously sieved throughout the workflows.

Top RPA use cases for Anti Money Laundering (AML) compliance

Today, banks and trade finance organizations are adopting AML compliance process tools that can be easily integrated in the core banking and core trade finance systems, right from screening customer data, validating information, compilation, monitoring, risk profiling, etc. To begin with, the non-initiated can always commence the AML crusade by incorporating RPA and AI/ML at strategic gateways.

Top AML RPA use cases

Listed here are some important RPA use cases for AML that can be implemented right away in baking and trade finance setups:

  • Legacy system integrations:
    Most institutions are still doing with legacy systems. Sieve data feeds from multiple legacy systems with RPA & AI/ML checkpoints, generate alerts that can be compared with negative listings and AML systems, such as FIRCO, OFAC, etc.

  • AML screening:
    Screen all new customers and their KYC information as well as account modifications for true match cases by running it through RPA & AI/ML enabled AML applications.

  • AML Data Sieving:
    Once suspected cases are listed check for true and false matches, generate suspicious transaction reports (STRs).

  • STR Filing
    Auto-download account numbers and other details for formally filing STR reports based on pre-configured rules. Formally file the cases with federal statutory agencies.

  • List Closures
    Use RPA to close the listed ids or accounts based on different AML filter checks.


In summary

Banks and financial institutions need to implement stricter AML measures to curb money laundering right at the point of origination and prevent the further damage to society. Technologies such as RPA & AI/ML enable institutions to continuously monitor their ecosystem in a highly automated manner.

Next reading

Topics:Robotic Process Automation (RPA)Banking, Financial Services, and Insurance (BFSI)

Subscribe to Blogs