How Banks Are Winning with AI and Automated Machine Learning
Banks have always relied on predictions to make their decisions. Estimating the risks or rewards of making a particular loan, for example, has traditionally fallen under the purview of bankers with deep knowledge of the industry and extensive expertise. But times are changing.
Today, banks realize that data science can significantly speed up these decisions with accurate and targeted predictive analytics. By leveraging the power of automated machine learning, banks have the potential to make data-driven decisions for products, services, and operations.
Read the white paper, How Banks Are Winning with AI and Automated Machine Learning, to find out more about how banks are tackling their biggest data science challenges.
In this white paper, we cover:
- The landscape of common AI use cases -- across every line of business and function in a bank
- How today’s banks can handle the data science talent shortage
- Case studies describing how organizations both large and small are leveraging automated machine learning
- Simple rules for spotting high-value use cases within your own organization
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The value of having a single platform that pulls all the components together can’t be underestimated. Then there’s the combination of the technology and the collaborative DataRobot team. If either one of those wasn’t there, I would have looked elsewhere.