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.

Financial institutions are faced with a common challenge to meet the ever-increasing demand from regulators to monitor and mitigate money laundering risk. Anti-Money Laundering (AML) Transaction Monitoring systems produce large volumes of work items, most of which do not result in quality investigations or actionable results. Backlogs of work items have forced some financial institutions to contract staffing firms to triage alerts spanning back months. Moreover, business analysts struggle to define interactions between AML models and to explain what attributes make a model productive. There is no one approach to solve this issue. Analysts need several analytical tools to explore model relationships, improve existing model performance, and add coverage for uncovered risk. This paper demonstrates an approach to improve existing AML models and focus money laundering investigations on cases that are more likely to be productive using analytical SAS^® tools including SAS^® Visual Analytics, SAS^® Enterprise Miner , SAS^® Studio, SAS/STAT^® software, and SAS^® Enterprise Guide^®.

As part of the Talanx Group, HDI Insurance has been one of the leading insurers in Brazil. Recently HDI Brazil implemented an innovative and integrated solution to prevent fraud in the Auto Claims process based on SAS^® Fraud Framework and SAS^® Real-time Decision Manager. A car fix or a refund is approved immediately after the claim registration for those customers who have no suspicious information. On the other hand, the high-scored claims are checked by the inspectors using SAS^® Social Network Analysis. In terms of analytics, the solution has a hybrid approach working with predictive models, business rules, anomalies, and network relationship. The main benefits are a reduction in the amount of fraud, more accuracy in determining the claims to be investigated, a decrease in the false-positive rate, and the use of a relationship network to investigate suspicious connections.