Machine Learning in Fintech

Webinar
DataRobot Machine Learning in Fintech Resource card v2.0 1

The financial services industry has been revolutionized by Fintech companies in recent years. Fintech have embraced new ways of funding, underwriting, and managing loans that are incredibly data-driven. Predictive and automated decision technologies, such as machine learning, are rapidly becoming best practice in financial services, embedded into the business processes of the most successful Fintech.

DataRobot Machine Learning in Fintech Resource card v2.0 1

In this 60-minute webinar, we'll show you:

  • The most common machine learning and automation use cases in Fintech
  • How automation allows Fintech to scale, control costs, and stay competitiv
  • The key factors for success in implementing automated machine learning

Speakers

colin priest
Colin Priest

VP, AI Strategy, DataRobot

  • DataRobot's platform makes my work exciting, my job fun, and the results more accurate and timely – it's almost like magic!
    Omair Tariq

    Data Analyst, Symphony Post Acute Network

  • I think we need to take it upon ourselves in the industry to build the predictive models that understand what the needs and wants of our customers are, and go through the whole curation process, become their concierge.
    Oliver Rees

    General Manager – Torque Data at Virgin Australia

  • At LendingTree, we recognize that data is at the core of our business strategy to deliver an exceptional, personalized customer experience. DataRobot transforms the economics of extracting value from this resource.
    Akshay Tandon
    Akshay Tandon

    VP of Strategy Analytics, LendingTree

  • DataRobot allows us to understand the data that’s being fed into our models without blindly feeding whatever we get into our system. DataRobot makes my team very effective.
    Deena Narayanaswamy
    Deena Narayanaswamy

    Head of Data Insights, Avant

  • We know part of the science and the heavy lifting are intrinsic to the DataRobot technology. Prior to working with DataRobot, the modeling process was more hands-on. Now, the platform has optimized and automated many of the steps, while still leaving us in full control. Without DataRobot, we would need to add two full-time staffers to replace what DataRobot delivers.
    Evariant