With increasing regulatory emphasis on using more scientific statistical processes and procedures in the Bank Secrecy Act/Anti-Money Laundering (BSA/AML) compliance space, financial institutions are being pressured to replace their heuristic, rule-based customer risk rating models with well-established, academically supported, statistically based models. As part of their customer-enhanced due diligence, firms are expected to both rate and monitor every customer for the overall risk that the customer poses. Firms with ineffective customer risk rating models can face regulatory enforcement actions such as matters requiring attention (MRAs); the Office of the Comptroller of the Currency (OCC) can issue consent orders for federally chartered banks; and the Federal Deposit Insurance Corporation (FDIC) can take similar actions against state-chartered banks. Although there is a reasonable amount of information available that discusses the use of statistically based models and adherence to the OCC bulletin Supervisory Guidance on Model Risk Management (OCC 2011-12), there is only limited material about the specific statistical techniques that financial institutions can use to rate customer risk. This paper discusses some of these techniques; compares heuristic, rule-based models and statistically based models; and suggests ordinal logistic regression as an effective statistical modeling technique for assessing customer BSA/AML compliance risk. In discussing the ordinal logistic regression model, the paper addresses data quality and the selection of customer risk attributes, as well as the importance of following the OCC's key concepts for developing and managing an effective model risk management framework. Many statistical models can be used to assign customer risk, but logistic regression, and in this case ordinal logistic regression, is a fairly common and robust statistical method of assigning customers to ordered classifications (such as Low, Medium, High-Low, High-Medium, and High-High risk). Using ordinal logistic regression, a financial institution can create a customer risk rating model that is effective in assigning risk, justifiable to regulators, and relatively easy to update, validate, and maintain.

Medicaid programs are the second largest line item in each state's budget. In 2012, they contributed $421.2 billion, or 15 percent of total national healthcare expenditures. With US health care reform at full speed, state Medicaid programs must establish new initiatives that will reduce the cost of healthcare, while providing coordinated, quality care to the nation's most vulnerable populations. This paper discusses how states can implement innovative reform through the use of data analytics. It explains how to establish a statewide health analytics framework that can create novel analyses of health data and improve the health of communities. With solutions such as SAS^® Claims Analytics, SAS^® Episode Analytics, and SAS^® Fraud Framework, state Medicaid programs can transform the way they make business and clinical decisions. Moreover, new payment structures and delivery models can be successfully supported through the use of healthcare analytics. A statewide health analytics framework can support initiatives such as bundled and episodic payments, utilization studies, accountable care organizations, and all-payer claims databases. Furthermore, integrating health data into a single analytics framework can provide the flexibility to support a unique analysis that each state can customize with multiple solutions and multiple sources of data. Establishing a health analytics framework can significantly improve the efficiency and effectiveness of state health programs and bend the healthcare cost curve.

Managing and organizing external files and directories play an important part in our data analysis and business analytics work. A good file management system can streamline project management and file organizations and significantly improve work efficiency . Therefore, under many circumstances, it is necessary to automate and standardize the file management processes through SAS^® programming. Compared with managing SAS files via PROC DATASETS, managing external files is a much more challenging task, which requires advanced programming skills. This paper presents and discusses various methods and approaches to managing external files with SAS programming. The illustrated methods and skills can have important applications in a wide variety of analytic work fields.