Monitoring server events to proactively identify future outages. Looking at financial transactions to check for money laundering. Analyzing insurance claims to detect fraud. These are all examples of the many applications that can use the power of SAS^® analytics to identify threats to a business. Using SAS^® Visual Investigator, users can now add a workflow to control how these threats are managed. Using the administrative tools provided, users can visually design the workflow that the threat would be routed through. In this way, the administrator can control the tasks within the workflow, as well as which users or groups those tasks are assigned to. This presentation walks through an example of using the administrative tools of SAS Visual Investigator to create a ticketing system in response to threats to a business. It shows how SAS Visual Investigator can easily be adapted to meet the changing nature of the threats the business faces.

Manufacturers of any product from toys to medicine to automobiles must create items that are, above all else, safe to use. Not only is this essential to long-term brand value and corporate success, but it's also required by law. Although perfection is the goal, defects are bound to occur, especially in advanced products such as automobiles. Automobiles are the largest purchase most people make, next to a house. When something that costs tens of thousands of dollars runs into problems, you tend to remember. Recalls in part reflect growing pains after decades of consolidation in the auto industry. Many believe that recalls are the culmination of years of neglect by manufacturers and the agencies that regulate them. For several reasons, automakers are acting earlier and more often in issuing recalls. In the past 20 years, the number of voluntarily recalled vehicles has steadily grown. The automotive-recall landscape changed dramatically in 2000 with the passage of the federal TREAD Act. Before that, federal law required that automakers issue a recall only when a consumer reported a problem. TREAD requires that companies identify potential problems and promptly notify the NHTSA. This is largely due to stricter laws, heavier fines, and more cautious car makers. This study helps automobile manufacturers understand customers who are talking about defects in their cars and to be proactive in recalling the product at the right time before the Government acts.

Read the paper (PDF) | View the e-poster or slides (PDF)

Historically, the risk and finance functions within a bank have operated within different rule sets and structures. Within its function, risk enjoys the freedom needed to properly estimate various types of risk. Finance, on the other hand, operates within the well-defined and structured rules of accounting, which are required for standardized reporting. However, the International Financial Reporting Standards (IFRS) newest standard, IFRS 9, brings these two worlds together: risk, to estimate credit losses, and finance, to determine their impact on the balance sheet. To help achieve this integration, SAS^® has introduced SAS^® Expected Credit Loss. SAS Expected Credit Loss enables customers to perform risk calculations in a controlled environment, and to use those results for financial reporting within the same managed environment. The result is an integrated and scalable risk and finance platform, providing the end-to-end control, auditability, and flexibility needed to meet the IFRS 9 challenge.

This end-to-end capability demonstration illustrates how SAS^® Viya can aid intelligence, homeland security, and law enforcement agencies in counter radicalization. There are countless examples of agency failure to apportion significance to isolated pieces of information which, in context, are indicative of an escalating threat and require intervention. Recent terrorist acts have been carried out by radicalized individuals who should have been firmly on the organizational radar. Although SAS^® products enable analysis and interpretation of data that enables the law enforcement and homeland security community to recognize and triage threats, intelligence information must be viewed in full context. SAS Viya can rationalize previously disconnected capabilities in a single platform, empowering intelligence, security, and law enforcement agencies. SAS^® Visual Investigator provides a hub for SAS^® Event Stream Processing, SAS^® Visual Scenario Designer, and SAS^® Visual Analytics, combining network analysis, triage, and, by leveraging the mobile capability of SAS, operational case management to drive insights, leads, and investigation. This hub provides the capability to ingest relevant external data sources, and to cross reference both internally held data and, crucially, operational intelligence gained from normal policing activities. This presentation chronicles the exposure and substantiation of a radical network and informs tactical and strategic disruption.

Applying solutions for recommending products to final customers in e-commerce is already a known practice. Crossing consumer profile information with their behavior tends to generate results that are more than satisfactory for the business. Natura's challenge was to create the same type of solution for their sales representatives in the platform used for ordering. The sales representatives are not buying for their own consumption, but rather are ordering according to the demands of their customers. That is the difference, because in this case the analysts does not have information about the behavior or preferences of the final client. By creating a basket product concept for their sales representatives, Natura developed a new solution. Natura developed an algorithm using association analysis (Market Basket) and implemented this directly in the sales platform using SAS^® Real-Time Decision Manager. Measuring the results in indications conversion (products added in the requests), the amount brought in by the new solution was 53% higher than indications that used random suggestions, and 38% higher than those that used business rules.

Implementation of state transition models for loan-level portfolio evaluation was an arduous task until now. Several features have been added to the SAS^® High-Performance Risk engine that greatly enhance the ability of users to implement and execute these complex, loan-level models. These new features include model methods, model groups, and transition matrix functions. These features eliminate unnecessary and redundant calculations; enable the user to seamlessly interconnect systems of models; and automatically handle the bulk of the process logic in model implementation that users would otherwise need to code themselves. These added features reduce both the time and effort needed to set up model implementation processes, as well as significantly reduce model run time. This paper describes these new features in detail. In addition, we show how these powerful models can be easily implemented by using SAS^® Model Implementation Platform with SAS^® 9.4. This implementation can help many financial institutions take a huge leap forward in their modeling capabilities.

Immediately after a new credit card product is launched and in the wallets of cardholders, sentiment begins to build. Positive and negative experiences of current customers posted online generate impressions among prospective cardholders in the form of technological word of mouth. Companies that issue credit cards can use sentiment analysis to understand how their product is being received by consumers, and, by taking suitable measure, can propel the card's market success. With the help of text mining and sentiment analysis using SAS^® Enterprise Miner and SAS^® Sentiment Analysis Studio, we are trying to answer which aspects of a credit card garnered the most favor, and conversely, which generated negative impressions among consumers. Credit Karma is a free credit and financial management platform for US consumers available on the web and on major mobile platforms. It provides free weekly updated credit scores and credit reports from the national credit bureaus TransUnion and Equifax. The implications of this project are as follows: 1) all companies that issue credit cards can use this technique to determine how their product is fairing in the market, and they can make business decisions to improve the flaws, based on public opinion; and 2) sentiment analysis can simulate the word-of-mouth influence of millions of existing users about a credit card.

In 1993, Erin Brockovich, a legal clerk to Edward L. Masry, began a lengthy manual investigation after discovering a link between elevated clusters of cancer cases in Hinkley, CA, and contaminated water in the same area due to the disposal of chemicals from a utility company. In this session, we combine disparate data sources - cancer cases and chemical spillages - to identify connections between the two data sets using SAS^® Visual Investigator. Using the map and network functionalities, we visualize the contaminated areas and their link to cancer clusters. What took Erin Brockovich months and months to investigate, we can do in minutes with SAS Visual Investigator.

When analyzing data with SAS^®, we often use the SAS DATA step and the SQL procedure to explore and manipulate data. Though they both are useful tools in SAS, many SAS users do not fully understand their differences, advantages, and disadvantages and thus have numerous unnecessary biased debates on them. Therefore, this paper illustrates and discusses these aspects with real work examples, which give SAS users deep insights into using them. Using the right tool for a given circumstance not only provides an easier and more convenient solution, it also saves time and work in programming, thus improving work efficiency. Furthermore, the illustrated methods and advanced programming skills can be used in a wide variety of data analysis and business analytics fields.

As a retailer, have you ever found yourself reviewing your last season's assortment and wondering, What should I have carried in my assortment ? You are constantly faced with the challenge of product selection, placement, and ensuring your assortment will drive profitable sales. With millions of consumers, thousands of products, and hundreds of locations, this question can often times be challenging and overwhelming. With the rise in omnichannel, traditional approaches just won't cut it to gain the insights needed to maximize and manage localized assortments as well as increase customer satisfaction. This presentation explores applications of analytics within marketing and merchandising to drive assortment curation as well as relevancy for customers. The use of analytics can not only increase efficiencies but can also give insights into what you should be buying, how best to create a profitable assortment, and how to engage with customers in-season to drive their path to purchase. Leveraging an analytical infrastructure to infuse analytics into the assortment management process can help retailers achieve customer-centric insights, in a way that is easy to understand, so that retailers can quickly take insights to actions and gain the competitive edge.

A microservice architecture prescribes the design of your software application as suites of independently deployable services. In this paper, we detail how you can design your SAS^® 9.4 programs so that they adhere to a microservice architecture. We also describe how you can leverage Many-Task Computing (MTC) in your SAS^® programs to gain a high level of parallelism. Under these paradigms, your SAS code will gain encapsulation, robustness, reusability, and performance. The design principles discussed in this paper are implemented in the SAS^® Infrastructure for Risk Management (IRM) solution. Readers with an intermediate knowledge of Base SAS^® and the SAS macro language will understand how to design their SAS code so that it follows these principles and reaps the benefits of a microservice architecture.

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

Whether you are a current SAS^® Marketing Optimization user who wants to fine tune your scenarios, a SAS^® Marketing Automation user who wants to understand more about how SAS Marketing Optimization might improve your campaigns, or completely new to the world of marketing optimizations, this session covers ideas and insights for getting the highest strategic impact out of SAS Marketing Optimization. SAS Marketing Optimization is powerful analytical software, but like all software, what you get out is largely predicated by what you put in. Building scenarios is as much an art as it is a science, and how you build those scenarios directly impacts your results. What questions should you be asking to establish the best objectives? What suppressions should you consider? We develop and compare multiple what-if scenarios and discuss how to leverage SAS Marketing Optimization as a business decisioning tool in order to determine the best scenarios to deploy for your campaigns. We include examples from various industries including retail, financial services, telco, and utilities. The following topics are discussed in depth: establishing high-impact objectives, with an emphasis on setting objectives that impact organizational key performance indicators (KPIs); performing and interpreting sensitivity analysis; return on investment (ROI); evaluating opportunity costs; and comparing what-if scenarios.

Production forecasts that are based on data analytics are able to capture the character of the patterns that are created by past behavior of wells and reservoirs. Future trends are a reflection of past trends unless operating principles have changed. Therefore, the forecasts are more accurate than the monotonous, straight line that is provided by decline curve analysis (DCA). The patterns provide some distinct advantages: they provide a range instead of an absolute number, and the periods of high and low performance can be used for better planning. When used together with DCA, the current method of using data driven production forecasting can certainly enhance the value tremendously for the oil and gas industry, especially in times of volatility in the global oil and gas industry.

Have you heard of SAS^® Customer Intelligence 360, the program for creating a digital marketing SasS offering on a multi-tenant SAS cloud? Were you mesmerized by it but found it overwhelming? Did you tell yourself, I wish someone would show me how to do this ? This paper is for you. This paper provides you with an easy, step-by-step procedure on how to create a successful digital web, mobile, and email marketing campaign. In addition to these basics, the paper points to resources that allow you to get deeper into the application and customize each object to satisfy your marketing needs.

Self-driving cars are no longer a futuristic dream. In the recent past, Google has launched a prototype of the self-driving car, while Apple is also developing its own self-driving car. Companies like Tesla have just introduced an Auto Pilot version in their newer version of electric cars which have created quite a buzz in the car market. This technology is said to enable aging or disabled people to remain mobile, while also increasing overall traffic saftery. But many people are still skeptical about the idea of self-driving cars, and that's our area of interest. In this project, we plan to do sentiment analysis on thoughts voiced by people on the Internet about self-driving cars. We have obtained the data from http://www.crowdflower.com/data-for-everyone which contain these reviews about the self-driving cars. Our dataset contains 7,156 observations and 9 variables. We plan to do descriptive analysis of the reviews to identify key topics and then use supervised sentiment analysis. We also plan to track and report how the topics and the sentiments change over time.

In 2012, US Customs scanned nearly 4% and physically inspected less than 1% of the 11.5 million cargo containers that entered the United States. Laundering money through trade is one of the three primary methods used by criminals and terrorists. The other two methods used to launder money are using financial institutions and physically moving money via cash couriers. The Financial Action Task Force (FATF) roughly defines trade-based money laundering (TBML) as disguising proceeds from criminal activity by moving value through the use of trade transactions in an attempt to legitimize their illicit origins. As compared to other methods, this method of money laundering receives far less attention than those that use financial institutions and couriers. As countries have budget shortfalls and realize the potential loss of revenue through fraudulent trade, they are becoming more interested in TBML. Like many problems, applying detection methods against relevant data can result in meaningful insights, and can result in the ability to investigate and bring to justice those perpetuating fraud. In this paper, we apply TBML red flag indicators, as defined by John A. Cassara, against shipping and trade data to detect and explore potentially suspicious transactions. (John A. Cassara is an expert in anti-money laundering and counter-terrorism, and author of the book Trade-Based Money Laundering. ) We use the latest detection tool in SAS^® Viya , along with SAS^® Visual Investigator.

Machine learning is not just for data scientists. Business analysts can use machine learning to discover rules from historical decision data or from historical performance data. Decision tree learning and logistic regression scorecard learning are available for standard data tables, and Associations Analysis is available for transactional event tables. These rules can be edited and optimized for changing business conditions and policies, and then deployed into automated decision-making systems. Users will see demonstrations using real data and will learn how to apply machine learning to business problems.

In industrial systems, vibration signals are the most important measurements for indicating asset health. Based on these measurements, an engineer with expert knowledge about the assets, industrial process, and vibration monitoring can perform spectral analysis to identify failure modes. However, this is still a manual process that heavily depends on the experience and knowledge of the engineer analyzing the vibration data. Moreover, when measurements are performed continuously, it becomes impossible to act in real time on this data. The objective of this paper is to examine using analytics to perform vibration spectral analysis in real time to predict asset failures. The first step in this approach is to translate engineering knowledge and features into analytic features in order to perform predictive modeling. This process involves converting the time signal into the frequency domain by applying a fast Fourier transform (FFT). Based on the specific design characteristics of the asset, it is possible to derive the relevant features of the vibration signal to predict asset failures. This approach is illustrated using a bearing data set available from the Prognostics Data Repository of the National Aeronautics and Space Administration (NASA). Modeling is done using R and is integrated within SAS^® Asset Performance Analytics. In essence, this approach helps the engineers to make better data-driven decisions. The approach described in this paper shows the strength of combining ex

More than ever, customers are demanding consistent and relevant interaction across all channels. Businesses are having to develop omnichannel marketing capabilities to please these customers. Implementing omnichannel marketing is often difficult, especially when using digital channels. Most products designed solely for digital channels lack capabilities to integrate with traditional channels that have on-premises processes and data. SAS^® Customer Intelligence 360 is a new offering that enables businesses to leverage both cloud and on-premises channels and data. This is possible due to the solution's hybrid cloud architecture. This paper discusses the SAS Customer Intelligence 360 approach to the hybrid cloud, and covers key capabilities on security, throughput, and integration.

Health care has long been focused on providing reactive care for illness, injury, or chronic conditions. But the rising cost of providing health care has forced many countries, health insurance payers, and health care providers to shift approaches. A new focus on patient value includes providing financial incentives that emphasize clinical outcomes instead of treatments. This focus also means that providers and wellness programs are required to take a segmentation approach to the population under their care, targeting specific people based on their individual risks. This session discusses the benefits of a shift from thinking about health care data as a series of clinical or financial transactions, to one that is centered on patients and their respective clinical conditions. This approach allows for insights pertaining to care delivery processes and treatment patterns, including identification of potentially avoidable complications, variations in care provided, and inefficient care that contributes to waste. All of which contributes to poor clinical outcomes.