Breaking Bad Data & Solving for AML
Authors: Abhishek Mehta & Eliud Polanco, and foreword by Brian O'Donnell, Executive in Residence, GRI
Mr. Abhishek Mehta and Mr. Eliud Polanco are independent contributors to the Global Risk Institute. They are solely responsible for the content of the article. Mr. Mehta and Mr. Polanco’s biographies are at the end of the article.
Over the past 18 months the Global Risk Institute has dedicated significant focus to both emerging regulatory reforms and emerging technology innovation. Last July our seminar on Big Data and Advanced Analytics emphasized the emergence of Fintech in assisting established firms in the financial industry to innovate existing processes. And at the GRI conference last fall we emphasized the role technology is playing in transforming the financial services industry; indeed, Lowell Bryan’s key note address highlighted unique opportunities for the Canadian Banks to utilize big data and advanced analytic solutions in order to leap ahead of other jurisdictions.
With this article we are kicking off a focus on the usage of Big Data and Advanced Analytics in the area of anti-moneylaundering (AML). AML has been a particularly difficult solution for global banks as the evolving regulatory standards call for banks to be able to readily monitor all transactions across the firm, which requires an in-depth knowledge of their clients and their clients’ counterparties (and often times the correspondent banks). This requirement lays bare a significant challenge facing most banks – with hundreds of systems and data bases, each with unique data models, unique customer identification protocols and differing data quality standards, AML monitoring and case work systems are overwhelmed. Indeed with an ever rising number of false positive alerts being triggered by such systems, banks are hiring thousands of case workers to manually resolve and close such alerts to the regulators satisfaction.
The GRI believes that finance technology, and specifically big data and advanced analytics, can be used to augment existing AML systems and significantly enhance both the effectiveness and efficiency of AML processes. By resolving client identities across the firm and using machine learning algorithms to monitor “unacceptable transactions,” advanced analytics can significantly reduce the number of “false positives,” and resolve those that do arise much more efficiently; by focusing on truly concerning patterns, advanced analytics is also more likely to identify truly concerning transactions.
Attached below is a white paper written by Abhi Mehta and Eliud Polanco of Tresata. They outline approaches that have had success with in helping large global banks drive greater efficiency and effectiveness in their AML process.
We plan to follow up this article with a seminar this fall, where we hope to bring the regulators and the Banks’ AML Executives together to discuss how Canada can embrace innovative technologies and evolve a regulatory “innovation sandbox”; the goal is to drive greater efficiency and effectiveness across AML systems and processes, and to have Canada take a leadership role in integrating Big Data and Advanced Analytics into key control processes.
About the Authors
Abhishek Mehta is the CEO & Co-founder of Tresata, a predictive intelligence software company that in a short span of 4 years, he has built into one of the most innovative big data companies in the world. Abhishek is recognized as one of the most influential thinkers, visionaries, and practitioners in the world of Big Data. His history is a rich combination of stints as a radical technology expert and a practical, in-the-trenches business leader. His experience includes Executive in Residence at MIT Media Lab, Managing Director at Financial institution of America, and various leadership positions at Cognizant Technology Solutions and Arthur Andersen.
A passionate supporter of entrepreneurship in the Southeast, Abhishek has been included in numerous lists of the top innovators, leaders, and disruptors of our generation. He is a highly sought after speaker on the topics of big data analytics, emerging business models, and all customary intersections of the two.
Eliud Polanco is the Chief Scientist at Tresata Money, with over 15 years’ experience in data analytics, business and technology strategy for financial institutions. Eliud has served as Global Head of Analytics and Big Data Strategy at multiple global systemically important financial institutions with responsibilities covering IT strategy and architecture, software partner management and build out of Data Science teams and analytics organizations. Analyses ranged from revenue and growth-oriented (Retail and Wholesale prospecting) to risk management and cost control (fraud intelligence, cybersecurity, financial stress test modeling and risk aggregation reporting). These experiences help shape Tresata’s approach to building real-world, innovative solutions that will truly deliver realizable and tangible business benefit to its Financial Service customers.