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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 ebook, 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 ebook, 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
DataRobot has really made the modeling process itself very easy. I've never had as much ease explaining the inner workings of my model as I did with DataRobot. Understanding is the first step to adoption, so I think that really went a long way for machine learning at LendingTree.
Akshay Tandon
Akshay Tandon

VP of Strategy Analytics, LendingTree

DataRobot's platform allows users to build and deploy highly accurate machine learning models in a fraction of the time it takes using traditional data science methods.
Loretta Ibanez
Loretta Ibanez

Freddie Mac Director, Mortgage Innovation

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