Greg Muenzen, Director, Novantas provides insights into the latest advancements in liquidity measurements and observations of current retail and commercial deposit trends from Novantas’ proprietary data sources.
Greg, thank you for taking the time to speak with us today. Please can you provide our readers with a quick overview of your experience and what your current professional focus is at the moment?
I have been a management consultant to the financial services industry for more than nine years, focusing primarily on treasury and risk issues. I have worked with financial institutions ranging from small community banks to international money center banks across the U.S., as well as larger institutions in Canada, Australia, and Latin America. My team specializes in analytically-focused engagements around liquidity, asset/liability management, interest-rate risk, stress testing and funds transfer pricing. A fundamental component of our work is the behavioral modeling of deposit and loan portfolios that require constant innovation in analytic and econometric approaches.
What are the benefits of attending a conference like Liquidity Risk Management USA and what can attendees expect to learn from your session?
The Liquidity Risk Management USA conference is a good opportunity to interact with and learn from a broad range of liquidity risk practitioners and other stakeholders from a range of financial institutions, regulators, and consultancies. My session will feature observations around current retail and commercial deposit trends from Novantas’s proprietary data sources, such as our Comparative Deposit Analytics and Commercial Deposit Study. I will also discuss insights from liquidity risk-focused engagements over the past year that are increasingly drawing on machine-learning techniques.
What role does machine-learning play with liquidity measurements? Is there a potential for banks to do more?
We are living in the age of big data, and as market interest rates rise, customer behavior is evolving. In that respect, machine-learning techniques can be useful for parsing large datasets to draw conclusions and automating the refresh and recalibration of those conclusions. For example, machine-learning algorithms can be used to make sense of account- and transaction-level data to understand which customer balance segments have higher or lower liquidity lives and how these behaviors are changing as rates rise.
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