PROVENIR AND MACHINE LEARNING
In one test of the Provenir Adaptor for Amazon Machine Learning, a developer with zero machine learning expertise built and deployed a machine learning credit risk model in 20 minutes, then operationalized the model in the Provenir Platform in a few hours. Previously such a task could have taken an expert IT team an entire month. While the Provenir Adaptor for Amazon Machine Learning is new, the Provenir Platform has been a stalwart for lenders for 20 years. The Platform’s ability to support all aspects of credit risk analysis is best seen through the experiences of its customers.
As shown in the previous pages of this paper, machine-learning techniques can greatly improve the overall speed and accuracy of credit risk determination. However the advanced analytics produced by machine learning models need two additional features to be truly useful:
And they must be part of a comprehensive platform designed to deliver credit risk decisions virtually in real time to meet the extreme demands of today’s business environment. They must support collaboration between business and IT users in a code-free environment.
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For these reasons, Provenir recently launched the Provenir Adaptor for Amazon Machine Learning, designedtooffer lendersall thebenefitsofmachine learning models without any of the complexity or cost of building and maintaining them. Moreover, this offering brings these machine learning benefits directly to the consoles of business analysts and lending officers – no IT intervention needed. This latest Provenir adaptor inputs credit predictive scores from the Amazon Machine Learning model directly into the risk decision process, which is fully automated by the Provenir Platform. The end result is that lenders are free to focus on what they do best while leaving the great complexity of machine learning algorithms and risk platforms to others. The visually configurable Provenir platform adds to the overall ease of use of this machine learning-as-a-service offering.
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