Anti-money laundering: managing regulatory risks

Data & Analytics, Risk & Compliance

Anti-money laundering programs: managing data, regulatory risks

A global multinational bank successfully managed large data volumes in its anti-money laundering program and protected clients and franchisees from regulatory risks.


A leading multinational bank


Identify and mitigate risks related to anti-money laundering (AML) regulations

Tools and technologies

Cloudera, Hadoop, Talend, Spark MLlib, MicroStrategy, Datameer, Sqoop


Our client, a multinational bank, had a comprehensive global program for anti-money laundering (AML) to protect its clients and franchisees from the risks of money laundering, terrorist financing and other financial crimes. To be able to do so effectively, they needed to deal with mounting volumes of data, which their existing applications could not handle. Their systems also generated a high number of false positives that increased the need for manual intervention.


We worked with the client’s global anti-money laundering program to develop a solution that provided them with consistent controls to identify AML risks and comply with relevant laws. We incorporated a modern data lake architecture, a centralized data hub that allowed the processing of increasing volumes of data from around the world. The solution we built was capable of handling data in petabytes.


We helped the multinational bank build a data lake that could hold 8 petabytes of data, much more than its existing data applications allowed. Next, Iris cross-trained the client’s global anti-money laundering team to ensure efficient use of the data lake in line with its global anti-money laundering program. 8 petabytes Data capacity 14 Global regions covered 5,000 End users

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