Last week Las Vegas was host to some of the brightest minds in the financial services sectors. Despite what you might think, these men and women were not there to beat the house, but to attend ACAMS 2019, the annual AML and financial crime conference hosted by the Association of Certified Anti-Money Laundering Specialists.
This year’s event showcased myriad fintech solutions, according to Jeff Nelson of Pitney Bowes, but the most important trend at the conference was the near-universal consensus that data quality is the key to beating financial crimes and helping financial institutions become compliant. “Data quality is a perpetual challenge for banks and other financial institutions with some organizations having upwards of 100 systems of record full of data to reconcile,” shared Nelson, Managing Director, Financial Crimes and Compliance at Pitney Bowes. “But with both new regulations like New York’s Department of Financial Services Regulation 504 and Canada’s FINTRAC putting the responsibility on banks to have a clear understanding of their customer and the data they hold about them, we’ve turned a corner in a decades-long conversation.”
The importance of data quality was also highlighted by the number of fintech solutions that are powered by machine learning (ML) and artificial intelligence (AI). “There has been an amazing growth in ML solutions for financial services in the last 18 months,” said Robert Smith, Managing Director, Financial Crimes and Compliance at Pitney Bowes. “While these solutions have great potential to deliver process efficiencies, the challenge is that if they don’t start out with good quality data to feed these tools, it’s simply a matter of garbage in, garbage out.”
While that might seem like a harsh statement, it is, according to Smith, necessary to speak plainly at this point. “Many financial services organizations are making investments in ML and AI. If they don’t get a handle on data quality now, then their investment will be wasted because the tools will not help AML teams build a complete view of their customers,” said Smith. Added Nelson “nor will they be able to access data required by regulators to satisfy today’s requirements, let alone when new regulations are introduced.” At the very least within the context of the new regulations Financial services organizations need to be able to quickly discover and profile specific types of data and apply specific treatments to the data. Ideally, they need to be able to ingest data in real-time throughout their overall compliance chain to achieve dynamic customer segmentation based on behavioral analysis. Without these capabilities the end result will be that financial institutions will have spent a lot of money on a solution that has provided little value and has still left them vulnerable to censure and significant fines.
But what Nelson and Smith observed on the show floor and in the sessions was that AML teams were looking to have conversations about data quality and looking at smart data quality tools before starting conversations with solution vendors. “It was heartening to talk to financial services leaders who are ready to talk about data quality and the challenges they face in keeping data organized as banks merge and grow via acquisitions,” said Smith. “A couple of years ago, this was definitely not the case.”
As financial services organizations move into a new era of accountability it’s clear that they’re also moving to a new understanding on the role that data quality and data governance plays in not only complying with regulations, but also stemming the tide of financial crimes. With high quality data readily at hand financial services institutions will have taken care of the heavy lift and be able to take advantage of the power of machine learning and artificial intelligence tools.