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FICO® Model Translator rapidly converts SAS Programming Language into a decision service for deployment into a broad range of platforms, including web services, Java, Spark and COBOL, making it easier to deploy advanced analytics solutions.
This provides organizations with greater accuracy, speed of deployment and reduced costs — helping companies easily integrate advanced analytics solutions into decisioning systems. This allows companies to use their data more effectively to improve real-time decisions with a customer focus.
View more on FICO® Blaze Advisor® decision rules management system.
Increase speed and accuracy
FICO Model Translator automates the conversion of SAS models into SRL or Java for faster operational integration with Blaze Advisor, eliminating code translation errors and reducing time to value for predictive models and advanced analytics.
Reduce licensing and training costs
FICO Model Translator eliminates the need for a separate SAS production environment, resulting in ~70% reduction in licensing costs while eliminating costly cross-platform training and support.
Greater transparency and control
FICO Model Translator puts the control and logic in the hands of business users while creating an audit trail necessary for compliance.
FICO Model Translator can be deployed in the cloud or on-premises with FICO Blaze Advisor or as a component of the FICO® Decision Management Suite, a cost-effective and easy way for customers to evaluate, customize, deploy and scale state-of-the-art analytics and decision management solutions.
Gain complete transparency that exposes the logic behind the models and records an audit trail with each transaction and execution to meet compliance requirements.
Automates model translation, testing and deployment.
Deploying new and revised models in minutes instead of weeks or months, greatly improving speed to market.
Give business analysts and stakeholders complete control over the life cycle of analytic models.
Significantly reduce cycle times for model deployment so more models can be deployed and enhanced quickly with fewer resources.
Eliminate manual coding errors and consistently deliver a 100% match with no discrepancies.
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