Using Machine Learning to Fight Money LaunderingInsights on Using New Technologies to Crack Down on Fraud
Machine learning can play a significant role in mitigating money laundering risks, says Andy Gandhi, managing director, data risk and compliance at the consultancy Alvarez and Marsal. (See: Leveraging Analytics for Fraud Audits)
See Also: What is next-generation AML?
"Different companies are attacking the problem from different perspectives," Gandhi says. Machine learning helps financial institutions to know their customers independent of their regular transactions, which helps in reducing fraud, he says.
In this video interview with Information Security Media Group, Gandhi also discusses:
- The difficulties of implementing machine learning at financial institutions;
- How other industries are leveraging machine learning to mitigate fraud;
- The challenges involved in cracking down on money laundering.
Gandhi is managing director, data risk and compliance, at the New York-based consultancy Alvarez and Marsal. He has more than 18 years of experience conducting and leading information risk investigations and compliance exercises, leveraging computer forensics, cybersecurity, e-discovery and data analysis expertise. He leads the firm's digital investigations group and co-leads its AML/BSA and KYC financial crime compliance initiatives.