21 October 2017
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Lantern Credit boosts machine earning engine through ARC library acquisition

07 March 2017  |  1611 views  |  0 Source: Lantern Credit

Lantern Credit, a financial technology company working to solve systematic inefficiencies in the consumer credit industry, is enhancing its proprietary machine learning engine, Beam AI, with the acquisition of the Abstract Regression-Classification (ARC) Machine Learning Library.

The machine learning library enables Lantern Credit 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. This benefits consumers and lenders with an improved consumer experience and a reduction in adverse action reporting. ARC will also be used to examine consumer intent and consumer interaction patterns to ensure the most relevant and timely education and credit related content is presented to the consumer.

“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. “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.”

In the field of machine learning, where computers learn from data instead of being explicitly programmed, the algorithms and models generated can be black-box, gray-box, or white-box. Black-box models are not human readable, gray-box models are somewhat but not entirely human readable, while white-box models are entirely human readable and are easily understood by auditors. White-box models are the preferred form of model whenever they can be obtained.

The ARC Machine Learning Library provides Beam AI with a commercial grade implementation of white-box Symbolic Regression, an advanced machine learning approach used to generate auditable models and transparency to the formulas that are used to build the models. This allows organizations to easily understand and verify the findings in a business setting.

“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. “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.”

In addition to acquiring the technology, Lantern Credit has appointed the creator of ARC, Michael Korns, as its Chief Data Scientist. Korns has a long and distinguished career in computer science research. Korns has previously served in leading scientific programs at IBM, Tymshare Transaction Services, Xerox Imaging and Investment Science. Korns’ current research interests include evolutionary computational complexity, machine learning and investment finance. 

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