Sovereign risk rating and country risk rating are conceptually distinct in that the former captures the risk of a country defaulting on its commercial debt obligations using economic variables while the latter covers the downside of a country's business environment including political and social variables alongside economic variables. Through this paper we would like to understand the differences between these risk approaches in assessing a country's credit worthiness by statistically examining the predictive power of political and social variables in determining country risk. To do this, we wish to build two models, first model with economic variables as regressors (sovereign risk model) and the second model with economic, political and social variables as regressors (country risk model) to compare the predictive power of regressors and model performance metrics between both the models. This will be an OLS regression model with country risk rating obtained from S&P as the target variable. With a general assumption that economic variables are driven by political processes and social factors, we would like to see if the second model has better predictive power. The economic, political and social indicators data that will be used as independent variables in the model will be obtained from world bank open data and target variable (country risk rating) will be obtained from S&P country risk ratings data.

Customer churn is an important area of concern that affects not just the growth of your company, but also the profit. Conventional survival analysis can provide a customer's likelihood to churn in the near term, but it does not take into account the lifetime value of the higher-risk churn customers you are trying to retain. Not all customers are equally important to your company. Recency, frequency, and monetary (RFM) analysis can help companies identify customers that are most important and most likely to respond to a retention offer. In this paper, we use the IML and PHREG procedures to combine the RFM analysis and survival analysis in order to determine the optimal number of higher-risk and higher-value customers to retain.

Understanding customer behavior profiles is of great value to companies. Customer behavior is influenced by a multitude of elements-some are capricious, presumably resulting from environmental, economic, and other factors, while others are more fundamentally aligned with value and belief systems. In this paper, we use unstructured textual cheque card data to model and estimate latent spending behavioral profiles of banking customers. These models give insight into unobserved spending habits and patterns. SAS^® Text Miner is used in an atypical manner to determine the buying segments of customers and the latent buying profile using a clustering approach. Businesses benefit in the way the behavioral spend model is used. The model can be used for market segmentation, where each cluster is seen as a target marketing segment, leads optimization, or product offering where products are specifically compiled to align to each customer's requirements. It can also be used to predict future spend or to align customer needs with business offerings, supported by signing customers onto loyalty programs. This unique method of determining the spend behavior of customers makes it ideal for companies driving retention and loyalty in their customers.

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Data that are gathered in modern data collection processes are often large and contain geographic information that enables you to examine how spatial proximity affects the outcome of interest. For example, in real estate economics, the price of a housing unit is likely to depend on the prices of housing units in the same neighborhood or nearby neighborhoods, either because of their locations or because of some unobserved characteristics that these neighborhoods share. Understanding spatial relationships and being able to represent them in a compact form are vital to extracting value from big data. This paper describes how to glean analytical insights from big data and discover their big value by using spatial econometric methods in SAS/ETS^® software.

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.

A/B testing is a form of statistical hypothesis testing on two business options (A and B) to determine which is more effective in the modern Internet age. The challenge for startups or new product businesses leveraging A/B testing are two-fold: a small number of customers and poor understanding of their responses. This paper shows you how to use the IML and POWER procedures to deal with the reassessment of sample size for adaptive multiple business stage designs based on conditional power arguments, using the data observed at the previous business stage.

For all business analytics projects big or small, the results are used to support business or managerial decision-making processes, and many of them eventually lead to business actions. However, executives or decision makers are often confused and feel uninformed about contents when presented with complicated analytics steps, especially when multi-processes or environments are involved. After many years of research and experiment, a web reporting framework based on SAS^® Stored Processes was developed to smooth the communication between data analysts, researches, and business decision makers. This web reporting framework uses a storytelling style to present essential analytical steps to audiences, with dynamic HTML5 content and drill-down and drill-through functions in text, graph, table, and dashboard formats. No special skills other than SAS^® programming are needed for implementing a new report. The model-view-controller (MVC) structure in this framework significantly reduced the time needed for developing high-end web reports for audiences not familiar with SAS. Additionally, the report contents can be used to feed to tablet or smartphone users. A business analytical example is demonstrated during this session. By using this web reporting framework based on SAS Stored Processes, many existing SAS results can be delivered more effectively and persuasively on a SAS^® Enterprise BI platform.

Detection and adjustment of structural breaks are an important step in modeling time series and panel data. In some cases, such as studying the impact of a new policy or an advertising campaign, structural break analysis might even be the main goal of a data analysis project. In other cases, the adjustment of structural breaks is a necessary step to achieve other analysis objectives, such as obtaining accurate forecasts and effective seasonal adjustment. Structural breaks can occur in a variety of ways during the course of a time series. For example, a series can have an abrupt change in its trend, its seasonal pattern, or its response to a regressor. The SSM procedure in SAS/ETS^® software provides a comprehensive set of tools for modeling different types of sequential data, including univariate and multivariate time series data and panel data. These tools include options for easy detection and adjustment of a wide variety of structural breaks. This paper shows how you can use the SSM procedure to detect and adjust structural breaks in many different modeling scenarios. Several real-world data sets are used in the examples. The paper also includes a brief review of the structural break detection facilities of other SAS/ETS procedures, such as the ARIMA, AUTOREG, and UCM procedures.

Creating an environment that enables and empowers self-service and agile analytic capabilities requires a tremendous amount of working together and extensive agreements between IT and the business. Business and IT users are struggling to know what version of the data is valid, where they should get the data from, and how to combine and aggregate all the data sources to apply analytics and deliver results in a timely manner. All the while, IT is struggling to supply the business with more and more data that is becoming available through many different data sources such as the Internet, sensors, the Internet of Things, and others. In addition, once they start trying to join and aggregate all the different types of data, the manual coding can be very complicated and tedious, can demand extraneous resources and processing, and can negatively impact the overhead on the system. If IT enables agile analytics in a data lab, it can alleviate many of these issues, increase productivity, and deliver an effective self-service environment for all users. This self-service environment using SAS^® analytics in Teradata has decreased the time required to prepare the data and develop the statistical data model, and delivered faster results in minutes compared to days or even weeks. This session discusses how you can enable agile analytics in a data lab, leverage SAS analytics in Teradata to increase performance, and learn how hundreds of organizations have adopted this concept to deliver self-service capabilities in a streamlined process.

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.

Credit card fraud. Loan fraud. Online banking fraud. Money laundering. Terrorism financing. Identity theft. The strains that modern criminals are placing on financial and government institutions demands new approaches to detecting and fighting crime. Traditional methods of analyzing large data sets on a periodic, batch basis are no longer sufficient. SAS^® Event Stream Processing provides a framework and run-time architecture for building and deploying analytical models that run continuously on streams of incoming data, which can come from virtually any source: message queues, databases, files, TCP\IP sockets, and so on. SAS^® Visual Scenario Designer is a powerful tool for developing, testing, and deploying aggregations, models, and rule sets that run in the SAS^® Event Stream Processing Engine. This session explores the technology architecture, data flow, tools, and methodologies that are required to build a solution based on SAS Visual Scenario Designer that enables organizations to fight crime in real time.

U.S. stock exchanges (currently there are 12) are tracked in real time via the Consolidated Trade System (CTS) and the Consolidated Quote System (CQS). CQS contains every updated quote from each of these exchanges, covering some 8,500 stock tickers. It provides the basis by which brokers can honor their fiduciary obligation to investors to execute transactions at the best price, that is, at the National Best Bid or Best Offer (NBBO). With the advent of electronic exchanges and high-frequency trading (timestamps are published to the nanosecond), data set size (approaching 1 billion quotes requiring 80 gigabytes of storage for a normal trading day) has become a major operational consideration for market behavior researchers re-creating NBBO values from quotes. This presentation demonstrates a straightforward use of hash tables for tracking constantly changing quotes for each ticker/exchange combination to provide the NBBO for each ticker at each time point in the trading day.

Tracking gains or losses from the purchase and sale of diverse equity holdings depends in part on whether stocks sold are assumed to be from the earliest lots acquired (a first-in, first-out queue, or FIFO queue) or the latest lots acquired (a last-in, first-out queue, or LIFO queue). Other inventory tracking applications have a similar need for application of either FIFO or LIFO rules. This presentation shows how a collection of simple ordered hash objects, in combination with a hash-of-hashes, is a made-to-order technique for easy data-step implementation of FIFO, LIFO, and other less likely rules (for example, HIFO [highest-in, first-out] and LOFO [lowest-in, first-out]).

Would you like to be more confident in producing graphs and figures? Do you understand the differences between the OVERLAY, GRIDDED, LATTICE, DATAPANEL, and DATALATTICE layouts? Finally, would you like to learn the fundamental Graph Template Language methods in a relaxed environment that fosters questions? Great this topic is for you! In this hands-on workshop, you are guided through the fundamental aspects of the GTL procedure, and you can try fun and challenging SAS^® graphics exercises to enable you to more easily retain what you have learned.

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Do you need to add annotations to your graphs? Do you need to specify your own colors on the graph? Would you like to add Unicode characters to your graph, or would you like to create templates that can also be used by non-programmers to produce the required figures? Great, then this topic is for you! In this hands-on workshop, you are guided through the more advanced features of the GTL procedure. There are also fun and challenging SAS^® graphics exercises to enable you to more easily retain what you have learned.

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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.

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.

From stock price histories to hospital stay records, analysis of time series data often requires use of lagged (and occasionally lead) values of one or more analysis variable. For the SAS^® user, the central operational task is typically getting lagged (lead) values for each time point in the data set. Although SAS has long provided a LAG function, it has no analogous lead function, which is an especially significant problem in the case of large data series. This paper 1) reviews the lag function, in particular, the powerful but non-intuitive implications of its queue-oriented basis; 2) demonstrates efficient ways to generate leads with the same flexibility as the LAG function, but without the common and expensive recourse to data re-sorting; and 3) shows how to dynamically generate leads and lags through use of the hash object.

Every organization, from the most mature to a day-one start-up, needs to grow organically. A deep understanding of internal customer and operational data is the single biggest catalyst to develop and sustain the data. Advanced analytics and big data directly feed into this, and there are best practices that any organization (across the entire growth curve) can adopt to drive success. Analytics teams can be drivers of growth. But to be truly effective, key best practices need to be implemented. These practices include in-the-weeds details, like the approach to data hygiene, as well as strategic practices, like team structure and model governance. When executed poorly, business leadership and the analytics team are unable to communicate with each other they talk past each other and do not work together toward a common goal. When executed well, the analytics team is part of the business solution, aligned with the needs of business decision-makers, and drives the organization forward. Through our engagements, we have discovered best practices in three key areas. All three are critical to analytics team effectiveness. 1) Data Hygiene 2) Complex Statistical Modeling 3) Team Collaboration

In the increasingly competitive environment for banks and credit unions, every potential advantage should be pursued. One of these advantages is to market additional products to your existing customers rather than to new customers, since your existing customers already know (and hopefully trust) you, and you have so much data on them. But how can this best be done? How can you market the right products to the right customers at the right time? Predictive analytics can do this by forecasting which customers have the highest chance of purchasing a given financial product. This paper provides a step-by-step overview of a relatively simple but comprehensive approach to maximize cross-sell opportunities among your customers. We first prepare the data for a statistical analysis. With some basic predictive analytics techniques, we can then identify those members who have the highest chance of buying a financial product. For each of these members, we can also gain insight into why they would purchase, thus suggesting the best way to market to them. We then make suggestions to improve the model for better accuracy.

Banks can create a competitive advantage in their business by using business intelligence (BI) and by building models. In the credit domain, the best practice is to build risk-sensitive models (Probability of Default, Exposure at Default, Loss Given Default, Unexpected Loss, Concentration Risk, and so on) and implement them in decision-making, credit granting, and credit risk management. There are models and tools on the next level that are built on these models and that are used to help in achieving business targets, setting risk-sensitive pricing, capital planning, optimizing Return on Equity/Risk Adjusted Return on Capital (ROE/RAROC), managing the credit portfolio, setting the level of provisions, and so on. It works remarkably well as long as the models work. However, over time, models deteriorate, and their predictive power can drop dramatically. As a result, heavy reliance on models in decision-making (some decisions are automated following the model's results-without human intervention) can result in a huge error, which might have dramatic consequences for the bank's performance. In my presentation, I share our experience in reducing model risk and establishing corporate governance of models with the following SAS^® tools: SAS^® Model Monitoring Microservice, SAS^® Model Manager, dashboards, and SAS^® Visual Analytics.

This presentation has the objective to present a methodology for interest rates, life tables, and actuarial calculations using generational mortality tables and the forward structure of interest rates for pension funds, analyzing long-term actuarial projections and their impacts on the actuarial liability. It was developed as a computational algorithm in SAS^® Enterprise Guide^® and Base SAS^® for structuring the actuarial projections and it analyzes the impacts of this new methodology. There is heavy use of the IML and SQL procedures.

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

People typically invest in more than one stock to help diversify their risk. These stock portfolios are a collection of assets that each have their own inherit risk. If you know the future risk of each of the assets, you can optimize how much of each asset to keep in the portfolio. The real challenge is trying to evaluate the potential future risk of these assets. Different techniques provide different forecasts, which can drastically change the optimal allocation of assets. This talk presents a case study of portfolio optimization in three different scenarios historical standard deviation estimation, capital asset pricing model (CAPM), and GARCH-based volatility modeling. The structure and results of these three approaches are discussed.

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^®.

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.

This session introduces how Equifax uses SAS^® to develop a streamlined model automation process, including development and performance monitoring. The tool increases modeling efficiency and accuracy of the model, reduces error, and generates insights. You can integrate more advanced analytics tools in a later phase. The process can apply in any given business problem in risk and marketing, which helps leaders to make precise and accurate business decisions.

AdaBoost (or Adaptive Boosting) is a machine learning method that builds a series of decision trees, adapting each tree to predict difficult cases missed by the previous trees and combining all trees into a single model. I discuss the AdaBoost methodology, introduce the extension called Real AdaBoost, which is so similar to stepwise weight of evidence logistic regression (SWOELR) that it might offer a framework with which we can understand the power of the SWOELR approach. I discuss the advantages of Real AdaBoost, including variable interaction and adaptive, stage-wise binning, and demonstrate a SAS^® macro that uses Real AdaBoost to generate predictive models.

Using zero inflated beta regression and gradient boosting, a solution to forecast the gross revenue of credit card products was developed. This solution was based on 1) A set of attributes from invoice information. 2) Zero inflated beta regression for forecasts of interchange and revolving revenue (by using PROC NLMIXED and by building data processing routines (with attributes and a target variable)). 3) Gradient boosting models for different product forecasts (annuity, insurance, etc.) using PROC TREEBOOST, exploring its parameters, and creating a routine for selecting and adjusting models. 4) Construction of ranges of revenue for policies and monitoring. This presentation introduces this credit card revenue forecasting solution.

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.

Ensemble models have become increasingly popular in boosting prediction accuracy over the last several years. Stacked ensemble techniques combine predictions from multiple machine learning algorithms and use these predictions as inputs to a second level-learning algorithm. This paper shows how you can generate a diverse set of models by various methods (such as neural networks, extreme gradient boosting, and matrix factorizations) and then combine them with popular stacking ensemble techniques, including hill-climbing, generalized linear models, gradient boosted decision trees, and neural nets, by using both the SAS^® 9.4 and SAS^® Visual Data Mining and Machine Learning environments. The paper analyzes the application of these techniques to real-life big data problems and demonstrates how using stacked ensembles produces greater prediction accuracy than individual models and na ve ensembling techniques. In addition to training a large number of models, model stacking requires the proper use of cross validation to avoid overfitting, which makes the process even more computationally expensive. The paper shows how to deal with the computational expense and efficiently manage an ensemble workflow by using parallel computation in a distributed framework.

Dealing with analysts and managers who do not know how to or want to use SAS^® can be quite tricky if everything you are doing uses SAS. This is where stored processes using SAS^® Enterprise Guide^® comes in handy. Once you know what they want to get out of the code, prompts can be defined in a smart and flexible way to give all users (whether they are SAS or not) full control over the output of the code. The key is having code that requires minimal maintenance and for you to be very flexible so that you can accommodate anything that the user comes up with. This session provides examples of credit risk stress testing where loss forecasting results were presented using different levels. Results were driven by a stored process prompt using a simple DATA step, PROC SQL, and PROC REPORT. This functionality can be used in other industries where data is shown using different levels of granularity.

Have you ever used a control chart to assess the variation in a process? Did you wonder how you could modify the chart to tell a more complete story about the process? This paper explains how you can use the SHEWHART procedure in SAS/QC^® software to make the following enhancements: display multiple sets of control limits that visualize the evolution of the process, visualize stratified variation, explore within-subgroup variation with box-and-whisker plots, and add information that improves the interpretability of the chart. The paper begins by reviewing the basics of control charts and then illustrates the enhancements with examples drawn from real-world quality improvement efforts.

This paper uses a simulation comparison to evaluate quantile approximation methods in terms of their practical usefulness and potential applicability in an operational risk context. A popular method in modeling the aggregate loss distribution in risk and insurance is the Loss Distribution Approach (LDA). Many banks currently use the LDA for estimating regulatory capital for operational risk. The aggregate loss distribution is a compound distribution resulting from a random sum of losses, where the losses are distributed according to some severity distribution and the number (of losses) distributed according to some frequency distribution. In order to estimate the regulatory capital, an extreme quantile of the aggregate loss distribution has to be estimated. A number of numerical approximation techniques have been proposed to approximate the extreme quantiles of the aggregate loss distribution. We use PROC SEVERITY to fit various severity distributions to simulated samples of individual losses from a preselected severity distribution. The accuracy of the approximations obtained is then evaluated against a Monte Carlo approximation of the extreme quantiles of the compound distribution resulting from the preselected severity distribution. We find that the second-order perturbative approximation, a closed-form approximation, performs very well at the extreme quantiles and over a wide range of distributions and is very easy to implement.

Transformation of raw data into sensible and useful information for prediction purposes is a priceless skill nowadays. Vast amounts of data, easily accessible at each step in a process, gives us a great opportunity to use it for countless applications. Unfortunately, not all of the valuable data is available for processing using classical data mining techniques. What happens if textual data is also used to create the analytical base table (ABT)? The goal of this study is to investigate whether scoring models that also use textual data are significantly better than models that include only quantitative data. This thesis is focused on estimating the probability of default (PD) for the social lending platform kokos.pl. The same methods used in banks are used to evaluate the accuracy of reported PDs. Data used for analysis is gathered directly from the platform via the API. This paper describes in detail the steps of the data mining process that is built using SAS^® Enterprise Miner . The results of the study support the thesis that models with a properly conducted text-mining process have better classification quality than models without text variables. Therefore, the use of this data mining approach is recommended when input data includes text variables.

Over the years, the use of SAS^® has grown immensely within Royal Bank of Scotland (RBS), making platform support and maintenance overly complicated and time consuming. At RBS, we realized that we have been living 'war and peace' every day for many years and that the time has come to re-think how we support SAS platforms. With our approach to rationalize and consolidate the ways our organization uses SAS came the need to review and improve the processes and procedures we have in place. This paper explains why we did it, what we've changed or reinvented, and how all these have changed our way of operation by bringing us closer to DevOps and helping us to improve our relationship with our customers as well as building trust in the service we deliver.

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.