Earnix and DataRobot Announce a Strategic Alliance
Earnix and DataRobot Announce a Strategic Alliance to Accelerate Adoption of Machine Learning and AI in the Financial Services Industry
The joint solution offers a seamless integration between DataRobot automated machine learning and the Earnix analytics platform.
InsurTech Connect – Las Vegas, October 1, 2018 – Earnix, Ltd., a leading provider of advanced analytics solutions for the financial services industry, and DataRobot, the pioneer in automated machine learning, announced a strategic alliance that delivers a new level of AI-driven insights in high-performance, real-time production systems. Through the use of best-in-class machine learning, users of the integrated solutions will dramatically increase the speed and accuracy of their analytic applications, enabling greater market responsiveness and improved business results.
The two companies announced the availability of a seamless integration between the solutions allowing models created in DataRobot’s automated machine learning platform to be easily integrated into the Earnix’s market leading pricing and risk modeling platform in a fraction of the time of traditional methods. Users of the Earnix solution will be able to combine DataRobot’s machine learning models with traditional regression analytical models. Marquee financial services customers are already using the integrated solutions in their applications and realizing greater precision and time-to-market.
According to Seann Gardiner, SVP of Business Development at DataRobot: “Accelerating AI success through best-in-class automated machine learning is what we are focused on. We’re excited to be working with Earnix, an innovator in analytics, to offer the financial services industry a best-of-breed solution that will give our joint customers a competitive advantage in the marketplace.”
According to Udi Ziv, Earnix CEO: “We are providing our clients with the tools, techniques, and technology needed to produce significant business results from advanced analytics. We are delighted to be working with DataRobot to empower users of all skill levels to develop and deploy highly accurate machine learning models within Earnix applications at scale. The joint solution delivers on our vision of Customer Centric Digital Transformation.”
For more information about the joint solution, including a video demo, solution brief and blog, please go here.
Earnix provides advanced analytics solutions designed for the financial services industry, which deliver significant results by integrating data-driven decision-making into the business process. We enable financial institutions to better compete in a new environment of highly personalized services by using advanced analytics to determine pricing and other offer components. Our integrated technology platform provides users with the most comprehensive set of tools, including machine learning capabilities, and is often connected to real-time production systems. Earnix has extensive experience providing solutions to the most sophisticated insurers and banks around the globe, and has a track record of empowering executives to act quickly and confidently, making a direct and measurable impact on their key performance indicators.
DataRobot offers an enterprise machine learning platform that empowers users of all skill levels to develop and deploy machine learning and AI faster. Incorporating a library of hundreds of the most powerful open source machine learning algorithms, the DataRobot platform automates, trains, and evaluates models in parallel, delivering AI applications at scale. DataRobot provides the fastest path to AI success for organizations of all sizes. For more information, visit datarobot.com.
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George Ravich, Earnix Chief Marketing Officer
Earnix provides an advanced analytics platform designed for the financial services industry, which integrates real-time decision-making capabilities into the business process, delivering significant results.
Earnix’s modeling, algorithms, and Machine Learning capabilities automate the rapid deployment of customer-centric offers by considering variables such as price, product features, and distribution channels, to optimize KPIs such as revenue, profit, sales volume, and customer satisfaction.
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