New DataRobot Release Extends Enterprise Readiness Capabilities and Automates Machine Learning in Insurance Industry Pricing Models
New Model Deployment Options, SAS Integration and Enterprise Readiness Features Will Drive Mass Adoption for Business Analysts, Actuaries, and Actuarial Data Scientists
"This is another significant step forward for the company," said
Bringing Enterprise-Ready Data Science to Business Users of All Skill Levels
The new release includes several new features to empower analysts of all skill levels to perform as data scientists, including:
- Integration with SAS: As the legacy analytics tool of choice for many companies, SAS is deeply embedded in many workflows.
DataRobot now ingests SAS files directly, lowering the barriers to working alongside SAS or replacing it altogether. This will smooth the transition for many organizations as they look to move beyond manual, time-consuming modeling approaches, and will de-risk their adoption of open source technologies. - Machine Learning-Powered Predictive Modeling for Insurance Pricing: DataRobot’s machine learning automation empowers insurance users to solve predictive modeling problems across marketing, distribution, underwriting, claims management and audit.
DataRobot is introducing Generalized Additive Models – a hybrid between easily interpretable Generalized Linear Models and highly accurate machine learning algorithms – to efficiently tackle pricing use cases. These newer sets of algorithms will allow users to solve pricing and risk segmentation problems more accurately, without sacrificing transparency or interpretability.DataRobot will also automatically generate rating tables that product managers and actuaries can directly submit to theDepartment of Insurance (DoI). - Scoring Code Generation:
DataRobot now offers the ability to export data preparation, preprocessing, and scoring code in Java – extending the deployment options beyond low-latency API and distributed scoring via Spark. Scoring code enables ultimate transparency and provides the flexibility to deployDataRobot models in virtually any environment.
“Today,
For more information on the new features and enhancements to the
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