UNIX and Linux SAS^® administrators, have you ever been greeted by one of these statements as you walk into the office before you have gotten your first cup of coffee? Power outage! SAS servers are down. I cannot access my reports. Have you frantically tried to restart the SAS servers to avoid loss of productivity and missed one of the steps in the process, causing further delays while other work continues to pile up? If you have had this experience, you understand the benefit to be gained from a utility that automates the management of these multi-tiered deployments. Until recently, there was no method for automatically starting and stopping multi-tiered services in an orchestrated fashion. Instead, you had to use time-consuming manual procedures to manage SAS services. These procedures were also prone to human error, which could result in corrupted services and additional time lost, debugging and resolving issues injected by this process. To address this challenge, SAS Technical Support created the SAS Local Services Management (SAS_lsm) utility, which provides automated, orderly management of your SAS^® multi-tiered deployments. The intent of this paper is to demonstrate the deployment and usage of the SAS_lsm utility. Now, go grab a coffee, and let's see how SAS_lsm can make life less chaotic.

A successful conversion to the International Financial Reporting Standards (IFRS) standard known as IFRS 9 can present many challenges for a financial institution. We discuss how leveraging best practices in project management, accounting standards, and platform implementation can overcome these challenges. Effective project management ensures that the scope of the implementation and success criteria are well defined. It captures all major decision points and ensures thorough documentation of the platform and how its unique configuration ties back directly to specific business requirements. Understanding the nuances of the IFRS 9 standard, specifically the impact of bucketing all financial assets according to their cash flow characteristics and business models, is crucial to ensuring the design of an efficient and robust reporting platform. Credit impairment is calculated at the instrument level, and can both improve or deteriorate. Changes in the level of credit impairment of individual financial assets enters the balance sheet as either an amortized cost, other comprehensive income, or fair value through profit and loss. Introducing more volatility to these balances increases the volatility in key financial ratios used by regulators. A robust and highly efficient platform is essential to process these calculations, especially under tight reporting deadlines and the possibility of encountering challenges. Understanding how the system is built through the project documentatio

The analytical data life cycle consists of 4 stages: data exploration, preparation, model development, and model deployment. Traditionally, these stages can consume 80% of the time and resources within your organization. With innovative techniques such as in-database and in-memory processing, managing data and analytics can be streamlined, with an increase in performance, economics, and governance. This session explores how you can optimize the analytical data life cycle with some best practices and tips using SAS^® and Teradata.

We are at a tipping point for credit risk modeling. To meet the technical and regulatory challenges of IFRS 9 and stress testing, and to strengthen model risk management, CBS aims to create an integrated, end-to-end, tools-based solution across the model lifecycle, with strong governance and controls and an improved scenario testing and forecasting capability. SAS has been chosen as the technology partner to enable CBS to meet these aims. A new predictive analytics platform combining well-known tools such as SAS^® Enterprise Miner , SAS^® Model Manager, and SAS^® Data Management alongside SAS^® Model Implementation Platform powered by SAS^® High-Performance Risk is being deployed. Driven by technology, CBS has also considered the operating model for credit risk, restructuring resources around the new technology with clear lines of accountability, and has incorporated a dedicated data engineering function within the risk modeling team. CBS is creating a culture of collaboration across credit risk that supports the development of technology-led, innovative solutions that not only meet regulatory and model risk management requirements but that set a platform for the effective use of predictive analytics enterprise-wide.