For far too long, anti-money laundering and terrorist financing solutions have forced analysts to wade through oceans of transactions and alerted work items (alerts). Alert-centered analysis is both ineffective and costly. The goal of an anti-money laundering program is to reduce risk for your financial institution, and to do this most effectively, you must start with analysis at the customer level, rather than simply troll through volumes of alerts and transactions. In this session, discover how a customer-centric approach leads to increased analyst efficiency and streamlined investigations. Rather than starting with alerts and transactions, starting with a customer-centric view allows your analysts to rapidly triage suspicious activities, prioritize work, and quickly move into investigating the highest risk customer activities.

When you were a kid, did you dress up as a unicorn for Halloween? Did you ever imagine you were a fairy living at the bottom of your garden? Maybe your dreams can come true! Modern-day marketers need a variety of complementary (and sometimes conflicting) skill sets. Forbes and others have started talking about unicorns, a rare breed of marketer--marketing technologists who understand both marketing and marketing technology. A good marketing analyst is part of this new breed; a unicorn isn't a horse with a horn but truly a new breed. It is no longer enough to be good at coding in SAS^®--or a whiz with Excel--and to know a few marketing buzzwords. In this session, we explore the skills an analyst needs to turn himself or herself into one of these mythical, highly sought-after creatures.

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Financial institutions rely heavily on quantitative models for risk management, balance-sheet stress testing and various business analyses and decision support functions. Investment decisions and business strategies are largely driven by estimates from models. Recent financial crises and model failures at high-profile banks have emphasized the need for better modeling practices. Regulators have stepped-in to assist banks with enhanced guidance and regulations for effective model risk management. Effective model risk management is more than developing a good model. SAS^® Model Risk Management provides a robust framework to capture and track model inventory. In this paper we present best practices in model risk management learned from implementation projects and interactions with industry experts. These best practices help firms that are setting up a model risk management framework or enhancing their existing practices.

The electrical grid has become more complex; utilities are revisiting their approaches, methods, and technology to accurately predict energy demands across all time horizons in a timely manner. With the advanced analytics of SAS^® Energy Forecasting, Old Dominion Electric Cooperative (ODEC) provides data-driven load predictions from next hour to next year and beyond. Accurate intraday forecasts means meeting daily peak demands saving millions of dollars at critical seasons and events. Mid-term forecasts provide a baseline to the cooperative and its members to accurately anticipate regional growth and customer needs, in addition to signaling power marketers where, when, and how much to hedge future energy purchases to meet weather-driven demands. Long-term forecasts create defensible numbers for large capital expenditures such as generation and transmission projects. Much of the data for determining load comes from disparate systems such as supervisory control and data acquisition (SCADA) and internal billing systems combined with external market data (PJM Energy Market), weather, and economic data. This data needs to be analyzed, validated, and shaped to fully leverage predictive methods. Business insights and planning metrics are achieved when flexible data integration capabilities are combined with advanced analytics and visualization. These increased computing demands at ODEC are being achieved by leveraging Amazon Web Services (AWS) for expanded business discovery and operational capacity. Flexible and scalable data and discovery environments allow ODEC analysts to efficiently develop and test models that are I/O intensive. SAS^® visualization for the analyst is a graphic compute environment for information-sharing that is memory intensive. Also, ODEC IT operations require deployment options tuned for process optimization to meet service level agreements that can be quickly evaluated, tested, and promoted into production. What was once very difficult for most ut ilities to embrace is now achievable with new approaches, methods, and technology like never before.

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There are standard risk metrics financial institutions use to assess the risk of a portfolio. These include well known measures like value at risk and expected shortfall and related measures like contribution value at risk. While there are industry-standard approaches for calculating these measures, it is often the case that financial institutions have their own methodologies. Further, financial institutions write their own measures, in addition to the common risk measures. SAS^® High-Performance Risk comes equipped with over 20 risk measures that use standard methodology, but the product also allows customers to define their own risk measures. These user-defined statistics are treated the same way as the built-in measures, but the logic is specified by the customer. This paper leads the user through the creation of custom risk metrics using the HPRISK procedure.

Electrolux is one of the largest appliance manufacturers in the world. Electrolux North America sells more than 2,000 products to end consumers through 9,000 business customers. To grow and increase profitability under challenging market conditions, Electrolux partnered with SAS^® to implement an integrated platform for SAS^® for Demand-Driven Planning and Optimization and improve service levels to its customers. The process uses historical order data to create a statistical monthly forecast. The Electrolux team then reviews the statistical forecast in SAS^® Collaborative Planning Workbench, where they can add value based on their business insights and promotional information. This improved monthly forecast is broken down to the weekly level where it flows into SAS^® Inventory Optimization Workbench. SAS Inventory Optimization Workbench then computes weekly inventory targets to satisfy the forecasted demand at the desired service level. This presentation also covers how Electrolux implemented this project. Prior to the commencement of the project, Electrolux and the SAS team jointly worked to quantify the value of the project and set the right expectations with the executive team. A detailed timeline with regular updates helped provide visibility to all stake holders. Finally, a clear change management strategy was also developed to define the roles and responsibilities after the implementation of SAS for Demand-Driven Planning and Optimization.

Does the rapidly changing fraud and compliance landscape make it difficult to adapt your applications to meet your current needs? Fraudsters are constantly evolving and your applications need to be able to keep up. No two businesses are ever the same. They all have different business drivers, customer needs, market demands, and strategic objectives. Using SAS^® Visual Investigator, business units can quickly adapt to their ever-changing needs and deliver what end users and customers need to be effective investigators. Using the administrative tools provided, users can quickly and easily adapt the pages and data structures that underpin applications to fit their needs. This presentation walks through the process of updating an insurance fraud detection system using SAS Visual Investigator, including changing the way alerts are routed to users, pulling in additional information, and building out application pages. It includes an example of how an end user can customize a solution due to changing fraud threats.

Oil and gas wells encounter many types of adverse events, including unexpected shut-ins, scaling, rod pump failures, breakthrough, and fluid influx, to name just a few common ones. This paper presents a real-time event detection system that presents visual reports and alerts petroleum engineers when an event is detected in a well. This system uses advanced time series analysis on both high- and low-frequency surface and downhole measurements. This system gives the petroleum engineer the ability to switch from passive surveillance of events, which then require remediation, to an active surveillance solution, which enables the engineer to optimize well intervention strategies. Events can occur simultaneously or rapidly, or can be masked over long periods of time. We tested the proposed method on data received from both simulated and publicly available data to demonstrate how a multitude of well events can be detected by using multiple models deployed into the data stream. In our demonstration, we show how our real-time system can detect a mud motor pressure failure resulting from a latent fluid overpressure event. Giving the driller advanced warning of the motor state and the potential to fail can save operators millions of dollars a year by reducing nonproductive time caused by these failures. Reducing nonproductive time is critical to reducing the operating costs of constructing a well and to improving cash flow by shortening the time to first oil.

SAS^® Customer Intelligence 360 is the new cloud-based customer data gathering application that uses the Amazon Web Services Cloud as a hosting platform. Learn how you can instrument your mobile application to gather usage data from your users as well as provide targeted application content based on either customer behavior or marketing campaigns.

SAS^® Event Stream Processing is designed to analyze and process large volumes of streaming data in motion. SAS Event Stream Processing provides a browser-based user interface that enables you to create and test event stream processing models in a visual drop-and-drag environment. This environment delivers a highly interactive and intuitive user experience. This paper describes the visual, interactive interface for building models and monitoring event stream activity. It also provides examples to demonstrate how you can easily build a model using the graphical user interface of SAS Event Stream Processing. In these examples, SAS Event Stream Processing serves as the front end to process high-velocity streams. On the back end, SAS^® Real-Time Decision Manager consumes events and makes the final decision to push the suited offer to the customer. This paper explains the concepts of windows, retention, edges, and connector. It also explains how SAS Event Stream Processing integrates with SAS Real-Time Decision Manager.

In the aftermath of the 2008 global financial crisis, banks had to improve their data risk aggregation in order to effectively identify and manage their credit exposures and credit risk, create early warning signs, and improve the ability of risk managers to challenge the business and independently assess and address evolving changes in credit risk. My presentation focuses on using SAS^® Credit Risk Dashboard to achieve all of the above. Clearly, you can use my method and principles of building a credit risk dashboard to build other dashboards for other types of risks as well (market, operational, liquidity, compliance, reputation, etc.). In addition, because every bank must integrate the various risks with a holistic view, each of the risk dashboards can be the foundation for building an effective enterprise risk management (ERM) dashboard that takes into account correlation of risks, risk tolerance, risk appetite, breaches of limits, capital allocation, risk-adjusted return on capital (RAROC), and so on. This will support the actions of top management so that the bank can meet shareholder expectations in the long term.

Real-time, integrated marketing solutions are a necessity for maintaining your competitive advantage. This presentation provides a brief overview of three SAS products (SAS^® Marketing Automation, SAS^® Real-Time Decision Manager, and SAS^® Event Stream Processing) that form a basis for building modern, real-time, interactive marketing solutions. It presents typical (and also possible) customer-use cases that you can implement with a comprehensive real-time interactive marketing solution, in major industries like finance (banking), telco, and retail. It demonstrates typical functional architectures that need to be implemented to support business cases (how solution components collaborate with customer's IT landscape and with each other). And it provides examples of our experience in implementing these solutions--dos and don'ts, best practices, and what to expect from an implementation project.

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Gone are the days when the only method of receiving a loan was by visiting your local branch and working with a loan officer. In today's economy, financial institutions increasingly rely on online channels to interact with their customers. The anonymity that is inherent in this channel makes it a prime target for fraudsters. The solution is to profile the behavior of internet banking in real time and assess each transaction for risk as it is processed in order to prevent financial loss before it occurs. SAS^® Visual Scenario Designer enables you to create rules, scenarios, and models, test their impact, and inject them into real-time transaction processing using SAS^® Event Stream Processing.

Does lyric complexity impact song popularity, and can analysis of the Billboard Top 100 from 1955-2015 be used to evaluate this hypothesis? The music industry has undergone a dramatic change. New technologies enable everyone's voice to be heard and has created new avenues for musicians to share their music. These technologies are destroying entry barriers, resulting in an exponential increase in competition in the music industry. For this reason, optimization that enables musicians and record labels to recognize opportunities to release songs with a likelihood of high popularity is critical. The purpose of this study is to determine whether the complexity of song lyrics impacts popularity and to provide guidance and useful information toward business opportunities for musicians and record labels. One such opportunity is the optimization of advertisement budgets for songs that have the greatest chance for success, at the appropriate time and for the appropriate audience. Our data has been extracted from open-source hosts, loaded into a comprehensive, consolidated data set, and cleaned and transformed using SAS^® as the primary tool for analysis. Currently our data set consists of 334,784 Billboard Top 100 observations, with 26,869 unique songs. The type of analyses used includes: complexity-popularity relationship, popularity churn (how often Billboard Top 100 resets), top five complexity-popularity relationships, longevity-complexity relationship, popularity prediction, and sentiment analysis on trends over time. We have defined complexity as the number of words in a song, unique word inclusion (compared to other songs), and repetition of each word in a song. The Billboard Top 100 represents an optimal source of data as all songs on the list have some measure of objective popularity, which enables lyric comparison analysis among chart position.

Every day, businesses have to remain vigilant of fraudulent activity, which threatens customers, partners, employees, and financials. Normally, networks of people or groups perpetrate deviant activity. Finding these connections is now made easier for analysts with SAS^® Visual Investigator, an upcoming SAS^® solution that ultimately minimizes the loss of money and preserves mutual trust among its shareholders. SAS Visual Investigator takes advantage of the capabilities of the new SAS^® In-Memory Server. Investigators can efficiently investigate suspicious cases across business lines, which has traditionally been difficult. However, the time required to collect, process and identify emerging fraud and compliance issues has been costly. Making proactive analysis accessible to analysts is now more important than ever. SAS Visual Investigator was designed with this goal in mind and a key component is the visual social network view. This paper discusses how the network analysis view of SAS Visual Investigator, with all its dynamic visual capabilities, can make the investigative process more informative and efficient.

Wouldn't it be fantastic to develop and tune scenarios in SAS^® Visual Scenario Designer and then smoothly incorporate them into your SAS^® Anti-Money Laundering solution with just a few clicks of your mouse? Well, now there is a way. SAS Visual Scenario Designer is the first data-driven solution for interactive rule and scenario authoring, testing, and validation. It facilitates exploration, visualization, detection, rule writing, auditing, and parameter tuning to reduce false positives; and all of these tasks are performed using point and click. No SAS^® coding skills required! Using the approach detailed in this paper, we demonstrate how you can seamlessly port these SAS Visual Scenario Designer scenarios into your SAS Anti-Money Laundering solution. Rewriting the SAS Visual Scenario Designer scenarios in Base SAS^® is no longer required! Furthermore, the SAS Visual Scenario Designer scenarios are executed on the lightning-speed SAS^® LASR™ Analytic Server, reducing the time of the SAS Anti-Money Laundering scenario nightly batch run. The results of both the traditional SAS Anti-Money Laundering alerts and SAS Visual Scenario Designer alerts are combined and available for display on the SAS^® Enterprise Case Management interface. This paper describes the different ways that the data can be explored to detect anomalous patterns and the three mechanisms for translating these patterns into rules. It also documents how to create the scenarios in SAS Visual Scenario Designer; how to test and tune the scenarios and parameters; and how alerts are ported seamlessly into the SAS Anti-Money Laundering alert generation process and the SAS Enterprise Case Management system.

SAS^® Customer Intelligence 360 enables marketers to create activity maps to delight customers with a consistent experience on digital channels such as web, mobile, and email. Activity maps enable the user to create a customer journey across digital channels and assign conversion measures as individual customers and prospects navigate the customer experience. This session details how those interactions are constructed in terms of what types of interactions tasks are supported and how those interaction tasks can be related to each other in a visual paradigm with built-in goal splits, analytics, wait periods, and A/B testing. Business examples are provided for common omni-channel problems such as cross-selling and customer segmentation. In addition, we cover the integration of SAS^® Marketing Automation and other marketing products, which enables sites to leverage their current segments and treatments for the digital channels.

Retailers and wholesalers invest heavily in technology, people, processes, and data to create relevant assortments across channels. While technology and vast amounts of data help localize assortments based on consumer preferences, product attributes, and store performance, it's impossible to complete the assortment planning process down to the most granular level of size. The ability to manage millions of size and store combinations is burdensome, not scalable, and not precise. Valuable time and effort is spent creating detailed, insightful assortments only to marginalize those assortments by applying corporate averages of size selling for the purchasing and distribution of sizes to locations. The result is missed opportunity: disappointed customers, lost revenue, and lost profitability due to missing sizes and markdowns on abundant sizes. This paper shows how retailers and wholesalers can transform historical sales data into true size demand and determine the optimal size demand profile to use in the purchasing and allocation of products. You do not need to be a data scientist, statistician, or hold a PhD to augment the business process with approachable analytics and optimization to yield game-changing results!

In Brazil, almost 70% of all loans are made based on pre-approved limits, which are established by the bank. Sicredi wanted to improve the number of loans granted through those limits. In addition, Sicredi wanted an application that focuses on the business user; one that enables business users to change system behavior with little or no IT involvement. The new system will be used in three major areas: - In the registration of a new client for whom Sicredi does not have a history. - Upon request by business users, after the customer already has a relationship with Sicredi, without customer request. - In the loan approval process, when a limit has not yet been set for the customer. The limit system will try to measure a limit for the customer based on the loan request, before sending the loan to the human approval system. Due to the impact of these changes, we turned the project into a program, and then split that program into three projects. The first project, which we have already finished, aimed to select an application that meets our requirements, and then to develop the credit measurement for the registration phase. SAS Real-Time Decision Manager was selected because it fulfills our requirements, especially those that pertain to business user operation. A drag-and-drop interface makes all the technical rules more comprehensible to the business user. So far, four months after releasing the project for implementation by the bank's branches, we have achieved more the USD 20 million granted in pre-approved loan limits. In addition, we have reduced the process time for limit measurement in the branches by 84%. The branches can follow their results and goals through reports developed in SAS Visual Analytics.

Omnichannel, and the omniscient customer experience, is most commonly used as a buzzword to describe the seamless customer experience in a traditional multi-channel marketing and sales environment. With more channels and methods of communication, there is a growing need to establish a more customer-centric way of dealing with all customer interactions, not only 1:1. Telenor, based out of Norway, is one of the world's major mobile operators with in excess of 200 million mobile subscriptions throughout 13 markets across Europe and Asia. The Norwegian home-market is a highly saturated and mature market in which customer demands and expectations are constantly rising. To deal with this and with increased competition, two major initiatives were established together with SAS^®. The initiatives aimed to leverage both the need for real-time analytics and decision management in our inbound channel, and for creating an omnichannel experience across inbound and outbound channels. The projects were aimed at both business-to-consumer (B2C) and business-to-business (B2B) markets. With significant legacy of back-end systems and a complex value chain it was important to both improve the customer experience and simplify customer treatment, all without impacting the back-end system at large. The presentation sheds light on how the projects worked to meet the technical challenges alongside the need for an optimal customer experience. With results far exceeding expectations, the outcome has established the basis for further Customer Lifecycle Management (CLM) initiatives to strengthen both Net Promoter Score/Customer loyalty and revenue.

For marketers who are responsible for identifying the best customer to target in a campaign, it is often daunting to determine which media channel, offer, or campaign program is the one the customer is more apt to respond to, and therefore, is more likely to increase revenue. This presentation examines the components of designing campaigns to identify promotable segments of customers and to target the optimal customers using SAS^® Marketing Automation integrated with SAS^® Marketing Optimization.

The Scheduler is an innovative tool that maps linear data (that is, time stamps) to an intuitive three-dimensional representation. This transformation enables the user to identify relationships, conflicts, gaps, and so on, that were not readily apparent in the data's native form. This tool has applications in operations research and litigation-related analyses. This paper discusses why the Scheduler was created, the data that is available to analyze the issue, and how this code can be used in other types of applications. Specifically, this paper discusses how the Scheduler can be used by supervisors to maintain the presence of three or more employees at all times to ensure that all federal- and state- mandated work breaks are taken. Additional examples of the Scheduler include assisting construction foremen to create schedules that visualize the presence of contractors in a logical sequence while eliminating overlap and ensuring cushions of time between contractors and matching items to non-uniform information.