Friday, October 5, 2012

Seamless Integration of Predictive Analytics and Business Rules

Operational deployment of predictive solutions includes exporting the data mining models you built in SAS, IBM SPSS, STASTISTICA, KNIME, R, ... into PMML, the Predictive Model Markup Language. Once in PMML standard, these models can be easily moved into production: on-site, in the cloud, Hadoop or in-database. Zementis offers a range of products that make this possible. These include the ADAPA Decisioning Engine and the Universal PMML Plug-in. Besides providing a predictive analytics engine, ADAPA also encapsulates a rules engine which allows for predictive models to be seamlessly integrated with business rules.

In this demo, we show a pre-qualification app that uses predictive models and rules to analyze the risk of mortgage default on loan applications. An application is accepted or referred for a variety of loan products depending on its perceived risk. ADAPA is the engine driving this application in the back-end.

Once logged in we use the ADAPA Web to download the mortgage solution files which are used throughout the demo. Predictive models expressed in PMML format are uploaded and verified in ADAPA along with rulesets expressed in tabular format. The ADAPA Web Console is used for managing predictive models, rulesets, and resource files as well as for batch-scoring. Real-time scoring is obtained via web-services or the Java API.

Finally, we show how the ADAPA Add-in for Excel is used to score data directly from within Excel. This part of the demo features the scoring of loan and tax data as well as the visualization of results via dashboards.

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