Artificial Intelligence, Machine Learning and Anti-Money Laundering (AI/ML & AML)

  1. Customer Due Diligence (CDD). Use cases within CDD include customer setup, onboarding, refresh and enhanced due diligence. During refresh, for example, RPA can be used to validate existing customer information, pulling customer data from various internal repositories to verify the customer’s information or to hand off to an associate for review. Combining several internal and external sources available for customer verification, RPA can be leveraged to search internal data repositories as well as approved third-party data sources for customer information. RPA can also automatically send emails to frontline staff and customer s, requesting necessary “Know Your Customer” (KYC) documentation.
  1. Customer Screening. RPA can help compile and consolidate customer information from multiple databases and hubs and send to screening vendors or compare directly to watch lists. RPA can also perform first level reviews and determine if screening results are “actual cases” or “false positives” based on predetermined business rules. As is the case with CDD, RPA can manage screening based on the customer’s risk level.
  2. Transaction Monitoring. The most important use case in transaction monitoring is “alert review”. Screening systems can generate thousands of duplicate alerts that must be examined individually by investigators. RPA can identify repeated alerts, check for changes in status, and take action to close without involving the investigator. RPA can also manage the data collection process for suspicious transaction alerts, handing off to associates to review or closing the alert on its own, based on predetermined business rules.
  1. Offboarding. Institutions determining when to close accounts or when to place a customer on a “Do Not Do Business” (DNDB) list can use RPA to check the client’s account status and provide insights on account activity. RPA can take over the manual process of updating restriction and closure codes to help reduce errors and automate mundane tasks. Based on business rules, RPA can proactively monitor and prevent transactions with specific clients on the DNDB list.




Data Scientist, Artificial Intelligence, Machine Learning, Author of “Artificial Intelligence with Python”

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Alberto Artasanchez

Alberto Artasanchez

Data Scientist, Artificial Intelligence, Machine Learning, Author of “Artificial Intelligence with Python”

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