Case Study 1

Anti Money Laundering for a Major Multinational Bank

PROBLEM STATEMENT:

Current rules-driven System is very cumbersome to maintain and is not able to keep up with new kinds of suspicious activity that is continuously evolving.

SOLUTION:

Machine Learning System that ingested millions of customer transactions over a 3-year time period was used to analyze the suspicious activity. A combination of Graph GAN + NEAT was used to build a predictive model for suspicious activity

BENEFITS:

Analyzes every transaction for suspicious activity and is able to flag suspicious behaviour at near real time

TECHNOLOGY:

Dataiku, Tensorflow, Python, Apache Spark

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