Advancing Financial Services Models Beyond GLMs hero banner
White Paper

Advancing Financial Services Models Beyond GLMs

Back in the 1990s, the financial services sector adopted generalized linear models (GLMs) because of their accuracy and the increasing complexity of decision-making. But GLMs suffer from several disadvantages. Specifically, they can be resource-intensive and time-consuming to build. They cannot work with missing values, and they require data to be collected from rigorously conducted experiments.

 

Enter automated machine learning and DataRobot’s automated machine learning platform. Our solution not only surmounts these obstacles but also provides more accurate, human-friendly explanations for how it uses the data, the patterns found in the data, and the reasons for a specific decision or prediction.

Read our white paper Advancing Financial Services Models Beyond GLMs , where we cover such topics as:

  • What is a GLM and how this modeling technique was not designed for the banking or insurance industries
  • How automated machine learning deals with missing values while also handling outliers in data
  • How machine learning deals with the non-linear impact of socio-economic features that are an important driver of both financial risk and marketing activity
DataRobot is an indispensable partner helping us maintain our reputation both internally and externally by deploying, monitoring, and governing generative AI responsibly and effectively.
Tom Thomas
Tom Thomas

VP of Data Strategy, Business Intelligence, & Analytics, FordDirect

The generative AI space is changing so fast but the flexibility, speed, and interoperability of DataRobot is helping us stay on the cutting edge. And, DataRobot’s team of GenAI experts have been true partners on our journey, helping us navigate the real concerns to apply generative AI in meaningful and safe ways.
Rosalia Tungaraza
Rosalia Tungaraza

Ph.D, AVP, Artificial Intelligence, Baptist Health South Florida

DataRobot provides us with innovative ways to test new ideas. Given a problem and a dataset, DataRobot allows us to generate multiple prototypes 20% faster. And the process facilitates the learning evolution of our data scientists.
Diego J. Bodas
Diego J. Bodas

Director of Advanced Analytics, MAPFRE ESPAÑA

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.
Craig Civil
Craig Civil

Director of Data Science & AI, BSI