Determining credit has been done by traditional techniques for decades. The challenge with traditional credit underwriting is that it doesn’t take into account all of the various aspects or features of an individual’s credit ability.  Underwrite.ai, a new credit startup, saw this as an opportunity to apply machine learning and AI to improve credit lending decisions. The company considers many more factors to construct a credit profile, which will help a lender make a decision that can be much more accurate.

By using H2O Driverless AI , an automatic machine learning platform, they were able to achieve better results than traditional credit scoring techniques. Here’s how – Driverless AI took in a large amount of data, determined relationships and patterns, and constructed a predictive model that considered approximately 763 different features. From Driverless AI, the company was able to automatically build the Adverse Action Report that lenders need in order to make a financial decision and do that within nanoseconds. Not only was this predictive model accurate, it was faster in making a decision too.

Marc Stein, underwrite.ai’s Founder and CEO emphasizes that “Not only can we reach a decision, we can also explain the decision and be fully compliant with the regulations. And it takes less than half a millisecond”. The platform provides the user with many visualizations and graphs to understand and explain the results. Because machine learning develops a non-linear model, it can be confusing to truly grasp the meaning derived from the data. With Driverless AI machine learning interpretability capability, feature interactions are not only understandable but explained.

How much better was the new credit risk model than traditional techniques? Well, the model had an accuracy of .95813, which according to the Marc Stein was ground-breaking: “This is the first time I’ve ever had a model at .95,” Marc Stein adds. After implementing Driverless AI, underwrite.ai has surpassed their target of producing a decision for a client in 20 milliseconds to producing a decision in half a millisecond.

“Using thousands of data points instead of the handful used by traditional credit scoring services, underwrite.ai generates a much more reliable lending risk profile for creditors to use when evaluating a credit or loan application,” explains Marc Stein. “By using Driverless AI to build and deploy our nonlinear models, we can provide lenders a fully automated process that delivers a highly reliable credit decision with a fully compliant explanation for the decision in less than half a millisecond. H2O and Driverless AI play an integral role in our platform, speeding up the development and deployment of models exponentially.”

Big thanks to AI and automatic machine learning for transforming our long-standing credit system.