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 webinar, H.P. Bunaes, General Manager of Financial Services at DataRobot, will provide an overview of how banks are learning from their data and using AI to tackle some of their biggest business challenges.
We will cover:
- Some of the highest value uses of AI in every line of business and function in a bank
- How banks can mitigate the data science talent shortage
- Case studies describing how leading practitioners are leveraging automated machine learning today
- Simple rules for spotting high-value use cases within your own organization
