About SAS Predictive Modeling
Organizations today are increasing their use of predictive analytics to more accurately predict their business outcomes, to improve business performance, and to increase profitability. Common and yet also highly strategic predictive modeling applications include fraud detection, rate making, credit scoring, customer retention, customer lifetime value, customer attrition/churn, and marketing response models. As the number of these models increases to support more and more business objectives, so does the requirement to manage these models reliably and securely as valuable corporate assets. Many companies, especially those in the financial services sector, also need to demonstrate adherence to model validation and governance practices outlined by the Office of the Comptroller of the Currency Administrator of National Banks (2000), the Basel II Committee’s Accord Implementation Group (Basel Committee on Banking Supervision 2005), and other governing bodies. This paper provides an overview of the model management life cycle process as well as an introduction to SAS Model Manager 2.1 with a focus on recommended practices for the improved management of predictive models in a production environment.
BUSINESS PROBLEMS
Before discussing the model life cycle management process, it is worthwhile to provide examples of key business pains faced by many corporations who develop and deploy large numbers of predictive models. While some corporations can go through the entire process of preparing the analytical base tables, developing models, and deploying the champion model in less than two months, many organizations might take 10 months or longer to deploy a champion model with some models never being deployed. Delays in deploying a model obviously result in lost opportunities and might even result in a model that no longer provides useful predictions due to changes in market and economic conditions. Model deployment for many organizations simply takes too long. Model deployment setbacks can often be attributed to technical integration issues. For example, many organizations lack a common integrated framework for comparing candidate models to select the champion model. Models are also often developed by more than one data modeler using a host of different algorithms along with potentially different development and test data sources which further add to the complexity of selecting models. The process used to develop the model along with the definition of the development and test data sources needs to be documented and accessible so that others can review and update the model as needed.
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