Anti-money laundering software saves $1M

Anti-money laundering software saves $1M

Banking

Unified AML proves to be a game changer

Global bank overcomes Anti-Money Laundering monitoring challenges and saves $1M in infrastructure costs with a unified front end.

Client

A top 5 global bank

Goal

Create a unified platform for anti-money laundering functions, analytics, and compliance implementations

Tools and technologies

Angular 5, Java, Open Shift, and DevOps

BUSINESS CHALLENGE

The client expanded its fraud and anti-money laundering (AML) monitoring functions, involving multiple lines of business and 15,000 employees. The scaled system led to the lack of standardization of frameworks and resultant adoption of disjointed, manual-intensive, and high-cost AML technology. The ongoing disconnect hindered the efforts of automating, consolidating; and implementing AML functions, enterprise analytics, and regulatory compliance efficiently throughout the organization.

SOLUTION

Iris optimized existing operations and technology investments by developing and implementing a unified point of access for the discrete AML functions, featuring micro-front-end architecture. Engineered to be horizontally scalable through containerization with common authentication and authorization gateways, the single user interface (UI) allows onboarding and control of multiple extended AML functions, including visualization of metrics.

OUTCOMES

The solution amplified efficiencies and reduced costs through the automated system and seamless exchanges of information. Significant outcomes included:
• Hassle-free transition from multiple to a single UI
• Unified, streamlined user experiences with more effective sessions
• Creation of standardized deployment procedures for AML rules and applications
• Saving of nearly $1M on infrastructure costs
• Reduced infrastructure maintenance time
• Frictionless migration of applications to the cloud

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Anti-money laundering: managing regulatory risks

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.

Client

A leading multinational bank.

Goal

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

Tools and technologies

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

BUSINESS CHALLENGE

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.

SOLUTION

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.

OUTCOMES

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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