TEL AVIV, Israel--(BUSINESS WIRE)--Earnix Ltd, a leading provider of predictive analytics solutions for the financial services industry, today announced the introduction of its Integrated Machine Learning technology, as an enhancement to the existing insurance software suite. This new capability is designed for demanding, high-performance real-time enterprise production systems, and will deliver a new level of market responsiveness and analytical sophistication to insurers. Several Earnix insurance clients have been using an early version of the technology and have seen significant improvements in their results.
The Analytics Arms Race
Analytics has become an arms race, as insurers around the globe seek to become more data-driven by operationalising real-time analytics and monetising new forms of data such as telematics and the Internet of Things (IoT). The addition of Integrated Machine Learning to the Earnix software suite enables users to excel in this environment, producing better and more accurate insights at speeds that only machine learning algorithms can produce.
Commenting on the new release, Earnix CEO Udi Ziv said: “We are providing financial institutions with the tools needed to more effectively compete in today’s data-driven real-time environment. Our new Integrated Machine Learning technology enables clients to rapidly move machine learning from the data scientist’s lab into operational processes. Clients who have been using the software are seeing measurable bottom line results.”
Designed for an Industry in Transition
Earnix’s new Integrated Machine Learning technology is designed for insurers at all levels of analytical maturity, who want real-time market responsiveness. From companies that are novices and need assistance in creating machine learning models, to expert users who can import proprietary algorithms that they have built, Earnix empowers all insurance providers to operationalise machine learning with a new level of predictive insights.
Evolution-Revolution: Enhancing Best Practices with New Technology
Integrated Machine Learning technology is able to combine traditional statistical modeling (for example General Linear Modeling or GLM), with cutting-edge machine learning techniques such as Random Forest and Gradient Boosting Machine. These hybrid models enhance trusted analytics that currently run business with the added power and capabilities of machine learning.
Machine Learning Deployed and Delivering Results
Earnix has been deploying early versions of its Integrated Machine Learning technology over the past 18 months with key insurer clients, who have realized significant benefits. Among the most impressive gains:
- Deciphering competitive price movements, especially important in the real-time marketplace environment of insurance aggregator sites.
- Developing highly personalised “next best offers”, which have resulted in significantly higher rates of purchase.
- Analyzing enormous volumes of telematics, IoT, and other emerging forms of data, which would not be possible without the speed of machine learning.
For more information on Earnix’s Integrated Machine Learning technology, please visit our website – which provides product information, blogs and videos on the subject.
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.