DataRobot complements Earnix with Machine Learning Capabilities for Decision Support
Get more detail on how Earnix and DataRobot enable machine learning automation, accuracy, and personalization at global financial institutions.
Watch the Earnix and DataRobot
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demo video on the DataRobot integration.
What the Joint Solution Provides
Earnix and DataRobot bring a new level of precision, insight, time to market, and a competitive edge to professionals at financial institutions around the world. The joint solution allows models created in the DataRobot platform to be easily integrated into the Earnix analytical platform.
Build and Deploy Machine Learning Models for Pricing, Product, and Personalization Projects
The joint solution empowers business analysts, data scientists, and actuaries to build and deploy state-of-the-art machine learning models within Earnix’s pricing and risk modeling platform in a fraction of the time of traditional modeling methods. Users of the Earnix solution then combine these machine learning models with traditional regression analytical models for improved analytical insight.
- A pricing professional can combine a machine learning model developed in DataRobot in place or combined with in a Generalized Linear Model (GLM) in Earnix to more accurately model risk and demand. Combining traditional GLM with machine learning models is an industry leading capability Earnix refers to as “hybrid modelling”.
- A product professional can import a Gradient Boosting Machines or other machine learning model to create better tailored product packages and coverages to be deployed from Earnix.
Download the Earnix and DataRobot Brochure to see how the integrated joint solution brings a new level of precision, insight, and speed to market, and competitive edge to your analytical pricing and product initiatives.
Benefits of the
Earnix – DataRobot Partnership
Analytical Decision Support with Machine Learning. For organizations with limited exposure to machine learning, leveraging DataRobot for model creation provides the ease-of-use and automation needed for beginning your machine learning initiatives. For more advanced organization, DataRobot provides a wide variety of model types and the breadth of model creation needed in today’s quick paced marketplace.
Speed Time to Market. Quickly take advantage of big data volumes, predict outcomes with added accuracy, and speed time to market. Being agile and responding to market trends is essential in today’s fast paced business environments.
Expand Machine Learning Breadth and Depth at Your Organization. The joint solution makes it easy for organizations to evolve their use of machine learning for modeling risk, demand, and propensity. Analysts can create more precise and robust analytical models that determine the perfect price and product combination, not just the preferred one.
Improved Scalability and Performance. Being able to build, test, and deploy machine learning models using Earnix and DataRobot together allows organizations to democratize the model creation process – making it easy to develop and test then deploy machine learning organization-wide. By automating this process using the joint solution, model creation, iteration, and performance is increased.
Use Best-in-Class Machine Learning Methods. The joint solution supports the use of hundreds of open source models, providing model diversity that is just not possible using a manual data science process. Sample model types include tree-based, linear, transformers, auto-tuned, feature selection, forest, and blender type models. This variety allows users to create machine learning models in DataRobot and then select and deploy them in Earnix to solve very specific use cases.
Quicker Processes with Less Rework. With Earnix and DataRobot, you can join multiple DataRobot models inside of the Earnix system and take pricing projects from development to production in a matter of seconds via the Earnix rating engine capabilities. Building and deploying machine learning models can be done in a fraction of the time it takes in manual coding platforms. With the ability to store and iterate upon existing models, less manual rework is required. This results in quicker time-to-insight.