Banks can deploy machine learning models in high-performance real-time production environments

More powerful analytics, vastly improving speed-to-market, monetising new forms of data

Earnix Ltd., a leading provider of predictive analytics solutions for the banking industry, today announced the introduction of its Integrated Machine Learning technology, as an enhancement to the existing banking 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 banks. Several Earnix 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 banks around the globe seek to become more data-driven by operationalising real-time analytics and monetising new forms of data such as 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 banks 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 banks 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.

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