How Banks are Winning with AI and Automated Machine Learning

Banks have always had to make predictions. Evaluating the risks and rewards of making a particular business loan, for example, requires estimating the probability that a borrower will default. Making these predictions traditionally required bankers to have deep knowledge of the borrower and their industry and extensive underwriting expertise. But times are changing. Today, banks realize they can significantly speed up decisions and take subjectivity and bias out of the process with predictive analytics. By leveraging their data, banks have the potential to make better, faster decisions to grow their business, improve the client experience, manage risk, and meet regulatory requirements efficiently. In this 20 minute overview, you’ll discover how banks are learning from their data and using AI to tackle some of their biggest business challenges.

Ryan Manville

AI Success Director at DataRobot

Ryan is a leader within the banking practice at DataRobot, helping financial institutions leverage AI and Machine Learning for predictive analytics and data mining. Ryan has over 25 years experience in management consulting and banking, with broad banking domain knowledge and deep expertise in data and analytics. Previous positions included COO of a retail bank, management consultant at McKinsey & Company and over a decade leading analytical teams at American Express. Ryan is a graduate of the Kellogg School of Management at Northwestern University where he received a MBA and Tufts University where he received a Bachelor of Science in Electrical Engineering

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