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Ashrith Barthur is a Security Scientist at H2O currently working on algorithms that detect anomalous behaviour in user activities, network traffic, attacks, financial fraud and global money movement. He has a PhD from Purdue University in the field of information security, specialized in Anomalous behaviour in DNS protocol.
ML(Machine Learning) in AML (Anti Money Laundering):
AML or anti money laundering has been a consistent bane of multiple governments and banks. A strong influences by countries to curb illegal money movement has resulted in a significant yet extremely small aspect of money laundering being identified – a success rate of about 2% average. A more global foot print the bank has the lesser is the accuracy of money laundering investigations. In its current mechanism, investigators analyse each money laundering alert and provide their subjective opinion towards a case. Unfortunately this takes time, and still has a return rate of about 2% at average and 10% at the highest. What we design are AI algorithms that work upon features that track monetary behaviour of every account. These features are essentially time-bound making them a fundamental aspect of algorithm design. The algorithms have a capability to improve the identification close to 70%, and we a certain exclusive features that are a function of time and improve much further.