Lantern Credit Buys Machine Learning Library To Enhance Offers

Lantern Credit, the financial technology company aiming to solve systematic inefficiencies in the consumer credit industry, announced Friday (March 10) that it has acquired the Abstract Regression-Classification (ARC) Machine Learning Library.

In a press release, the company said the purchase is aimed at enhancing its proprietary machine learning engine, Beam AI. Lantern Credit said the machine learning library enables the company to use a human-machine hybrid learning approach that incorporates human guidance in the machine learning training process to produce more reliable outputs.

Lantern Credit’s Beam AI will use the symbolic regression technology to ensure that credit offers presented to consumers are actionable and timely, the company said in the release. By doing that, the consumers and lenders will get an improved consumer experience and a reduction in adverse action reporting. ARC will also be used to examine consumer intent and interaction patterns to make sure they are served up the most relevant and timely education and credit-related content.

“Leveraging the ARC software to advance the Beam AI technology produces the most advanced artificial intelligence (AI) and machine learning application in the consumer credit management space,” said Chad Swensen, CEO of Lantern Credit, in the release. “This will enable us to help financial institutions provide credit offers that are relevant, while providing information that empowers consumers to improve their overall financial wellness.”

The ARC Machine Learning Library gives Beam AI a commercial-grade implementation of white box symbolic regression. “Our Beam AI is based on nonlinear symbolic regression and is the first white box machine learning technology productized for consumer credit finance,” said John Sculley, vice chairman of Lantern Credit, in the same press release. “We enable our bank partners to monetize their massive consumer data with actionable analytic predictions and better manage risk of lending decisions based on specific underwriting requirements.”