Powering Efficient, Effective Forecasting Through ML and APIs
As a task that is both vital and challenging for treasury, cash forecasting is an area where leveraging effective tools can make a massive difference. Forecasting using spreadsheets consumes more time than most treasury departments can afford, and human errors and inaccuracy can often still plague the final results.
This webinar will discuss how machine learning (ML) and application programming interfaces (APIs) can power more efficient and effective forecasting. Details covered will include pattern detection and analysis through ML, APIs and aggregation, and automation and updating, as well as discussing technological expansions into scenario planning and auto-tagging.
Tracey brings over 20 years of treasury FinTech experience across a wide variety of disciplines including sales, consulting, and training. Most recently, Tracey held key roles in building and developing sales and pre-sales teams to bring artificial intelligence/machine learning to the long underserved problem of direct cash forecasting. Her combination of both practitioner and vendor experience helps her understand the unique issues companies face and present practical solutions for digital transformation. Currently residing in the Dallas-Ft. Worth Metroplex, she holds a B.S. in Economics from the University of Pennsylvania’s Wharton School of Business.