This presentation addresses two main topics: The first topic focuses on the industry's norms and the best practices for building internal credit ratings (PD, EAD, and LGD). Although there is not any capital relief to local US banks using internal credit ratings (the US hs not adopted the Internal Rating Based approach of Basel2, with the exception of the top 10 banks), there is an increased responsiveness in credit ratings modeling for the last two years in the US banking industry. The main reason is the added value a bank can achieve from these ratings, and that is the focus of the second part of this presentation. It describes our journey (a client story) for getting there, introducing the SAS^® project. Even more importantly, it describes how we use credit ratings in order to achieve effective credit risk management and get real added value out of that investment. The key success factor for achieving it is to effectively implement ratings within the credit process and throughout decision making . Only then can ratings be used to improve risk-adjusted return on capital, which is the high-end objective of all of us.

Organizations today make numerous decisions within their businesses that affect almost every aspect of their daily operations. Many of these decisions are now automatically generated by sophisticated enterprise decision management systems. These decisions include what offers to make to customers, sales transaction processing, payment processing, call center interactions, industrial maintenance, transportation scheduling, and thousands of other applications that all have a significant impact on the business bottom line. Concurrently, many of these same companies have developed or are now developing analytics that provide valuable insight into their customers, their products, and their markets. Unfortunately, many of the decision systems cannot maximize the power of analytics in the business processes at the point where the decisions are made. SAS^® Decision Manager is a new product that integrates analytical models with business rules and deploys them to operational systems where the decisions are made. Analytically driven decisions can be monitored, assessed, and improved over time. This paper describes the new product and its use and shows how models and business rules can be joined into a decision process and deployed to either batch processes or to real-time web processes that can be consumed by business applications.

Are you wondering what is causing your valuable machine asset to fail? What could those drivers be, and what is the likelihood of failure? Do you want to be proactive rather than reactive? Answers to these questions have arrived with SAS^® Predictive Asset Maintenance. The solution provides an analytical framework to reduce the amount of unscheduled downtime and optimize maintenance cycles and costs. An all new (R&D-based) version of this offering is now available. Key aspects of this paper include: Discussing key business drivers for and capabilities of SAS Predictive Asset Maintenance. Detailed analysis of the solution, including: Data model Explorations Data selections Path I: analysis workbench maintenance analysis and stability monitoring Path II: analysis workbench JMP^®, SAS^® Enterprise Guide^®, and SAS^® Enterprise Miner^™ Analytical case development using SAS Enterprise Miner, SAS^® Model Manager, and SAS^® Data Integration Studio SAS Predictive Asset Maintenance Portlet for reports A realistic business example in the oil and gas industry is used.