The life sciences industry is under constant pressure to deliver safe, effective treatments faster than ever before. Traditional clinical trial processes, while rigorous, often face delays due to data complexity, regulatory requirements, and operational inefficiencies. Enter AI and advanced analytics—game-changing technologies that are transforming drug development and clinical research.   The Role of AI in Modern Life Sciences AI in life sciences is a practical solution driving measurable impact across the drug development lifecycle. From early discovery to regulatory submission, AI enables organizations to:   Predict trial outcomes using machine learning models trained on historical and real-world data. Accelerate patient recruitment by analyzing electronic health records and identifying eligible participants. Optimize trial design through simulation analytics, reducing costly amendments and delays.   Generative AI: Automating Complex Workflows Generative AI (GenAI) is revolutionizing clinical research operations. Imagine automating:   Creation of SDTM and ADaM datasets. Code validation and compliance checks. Drafting FDA submission documents. These capabilities not only save time but also minimize human error, ensuring quality and regulatory adherence.   Data Privacy and Synthetic Data Patient privacy remains paramount. AI-driven synthetic data generation allows researchers to fill gaps in datasets without compromising confidentiality. This approach supports robust modeling while meeting strict privacy regulations.   SAS Solutions for Life Sciences SAS provides a comprehensive platform tailored for life sciences analytics:   SAS Life Science Analytics Framework: A unified AI and data platform that accelerates clinical development. Embedded AI tools: Automate repetitive tasks and enhance decision-making. Simulation and real-world data analytics: Improve trial efficiency and predictability. Cloud-native architecture: Enables scalability and compliance across global research teams.   Looking Ahead The future of clinical trials is data-driven, AI-powered, and patient-centric. Organizations that embrace these technologies will not only accelerate development timelines but also improve outcomes for patients worldwide. To learn more about how SAS can help, navigate to the following pages: Clinical Trial Data & Research Analytics Software and AI in Life Sciences.     Find more articles from SAS Global Enablement and Learning here. ... View more

In the world of life sciences, clinical trials are the backbone of medical innovation — turning research into therapies that save lives. But running a trial involves managing vast amounts of data, navigating strict regulatory standards, and translating numbers into actionable insight. That’s where SAS comes in.   Why SAS for Clinical Research Analytics? Life sciences companies trust SAS to meet regulatory requirements and deliver new therapies to submission faster and more efficiently. For someone looking to build a career in clinical trials (especially on the data/analytics side), that means two things: (1) there’s demand for professionals who can operate in this ecosystem, and (2) there’s a clear technology stack and methodology to learn that aligns with industry needs.   Enter the Learning Path: SAS Clinical Trials Learning Subscription For anyone aiming to become a clinical trials professional, the SAS Clinical Trials Learning Subscription offers a structured, hands-on pathway. The subscription includes three learning paths, each designed to build skills progressively:   Path to SDTM Programming  Gain foundational programming skills with SAS. Learn about the trial lifecycle, CDISC SDTM data standards, and regulatory expectations. Understand how raw trial data become SDTM domains ready for submission to regulatory authorities.   Select any image to see a larger version. Mobile users: To view the images, select the "Full" version at the bottom of the page.   Translate SDTM to ADaM Learn how to turn SDTM domains into analysis-ready datasets. Gain Macro programming knowledge to build SDTM domains and ADaM datasets even more efficiently.     Analyze, Visualize, & Report Learn statistical analysis and modeling relevant to clinical trials – i.e., ANOVA, Regression, t tests, etc. Master data visualization techniques — i.e., ODS Graphics. Develop reporting skills for tables, listings, and figures suitable for regulatory submissions. Gain the ability to turn complex trial data into actionable insights for decision-making.     By completing these three paths, learners gain end-to-end expertise: from understanding trial design, to managing and preparing data, to analyzing results and presenting them effectively.   Why This Subscription Works for a Career Trajectory Here’s how the Learning Subscription can provide a roadmap toward your career goals:   Phase 1: Domain & Basics – Learn what clinical research in the pharmaceutical/biotech/life sciences world requires—trial phases, regulatory constraints, data standards. Take the early courses in the subscription: SAS Programming 1 (Essentials), SAS Programming 2 (Data Manipulation), SAS SQL 1. Build foundational skills. Phase 2: Clinical Data Standards – Move into SAS Programming for Clinical Trials 1: SDTM, and SAS Programming for Clinical Trials 2: ADaM. Understand how to build proper domains and analysis datasets. Additionally, understand how Macro Programming can lead to more effective programming for SDTM and ADaM. Phase 3: Bridge to Analytics & Insight – Learn how to go beyond “just coding” to deriving insight, ensuring data integrity, supporting submissions and making decisions. Apply statistical analysis, create visualizations, and develop regulatory-compliant reporting skills. Phase 4: Career Readiness – With knowledge gained from the subscription, you can pursue the SAS Clinical Trials Programming Certification. By combining domain knowledge + technical tools + analytics mindset + SAS credentials, you position yourself for clinical trials professional roles such as clinical data manager, SAS clinical programmer, biostatistician, trial analytics specialist, etc. Phase 5: Stand Out – Many in the field may know SAS or basic programming. However, knowing CDISC standards, SDTM/ADaM workflows, regulatory submission environments, and analytics platforms like SAS give you an edge.   The Competitive Advantage By aligning yourself with both the domain (clinical trials) and the tool ecosystem (SAS + CDISC + analytics), you become a bridge between data, programming, and the business of clinical research. Employers increasingly look for:   Programmers who know CDISC standards and can produce SDTM/ADaM datasets. Analysts who can work in validated, regulatory‐compliant environments. Teams who can draw insights from data, not just run code.   Final Thoughts The SAS Clinical Trials Learning Subscription is a powerful route to becoming a professional in clinical trials. With all three paths, learners can now complete the full cycle: from data capture to insight delivery.   For anyone passionate about science, data, and making a difference in healthcare, this pathway is both practical and rewarding. Learn SAS, master trial workflows, analyze and visualize data, and prepare to step into a career that drives real-world impact.     Find more articles from SAS Global Enablement and Learning here. ... View more

In today’s rapidly evolving financial landscape, model risk management (MRM) has become a cornerstone of responsible governance. With the AI, machine learning (ML), and generative AI (GenAI), financial institutions face increasing complexity in their modeling systems. SAS offers a comprehensive solution to help organizations manage this effectively.   The Expanding Scope of Model Risk Modern models, especially those powered by AI, GenAI, and ML, offer unprecedented predictive power. However, they also introduce risks such as bias, opacity, and regulatory scrutiny. Financial institutions face challenges like:   Fragmented governance across siloed model development environments. Lack of integrated systems to manage model metadata and documentation. Difficulty adapting to evolving regulatory requirements.   SAS’s End-to-End Model Risk Management Solution SAS provides a comprehensive MRM solution that centralizes model governance and streamlines the entire model life cycle. Key capabilities of SAS Model Risk Management include:   Centralized Model Inventory SAS enables institutions to establish a centralized repository for all models—regardless of type or technology. This inventory supports internal policies and includes:   Permissions and version control. Metadata import (e.g., validation results, criticality ratings). Integration with third-party systems.   Robust Documentation & Tracking Models are documented and tracked throughout their life cycle, with a single source of truth for:   Model lineage and ownership. Validation and review processes. Business line associations and version history.   Select any image to see a larger version. Mobile users: To view the images, select the "Full" version at the bottom of the page.   Insightful Risk Analysis SAS empowers users to analyze thousands of data points—both quantitative and qualitative—to uncover model risk concentrations. Interactive dashboards and visualizations enhance institutional awareness and support board-level oversight.     Agile Governance Tools SAS’s platform helps institutions stay ahead of changing risk policies and regulations by:   Enforcing signoffs, legal reviews, and testing. Supporting flexible workflows for validation and change management. Providing enterprise-level oversight across all risk categories.     Comprehensive Feature Set SAS Model Risk Management includes:   Data governance & metadata management with integrated business glossaries and lineage visualization. Model usage tracking and change management capabilities. Dashboards for data exploration and reporting. Complete workflow management for any model type or source.   Industry Recognition SAS was ranked #2 overall in the Chartis RiskTech100® 2025, winning six category awards including:   AI for Banking Balance Sheet Risk Management Behavioral Modeling Enterprise Stress Testing IFRS 9 Model Risk Management   Conclusion As financial institutions navigate the complexities of modern modeling, SAS offers a powerful, integrated solution to manage model risk effectively. With centralized governance, automated monitoring, and deep insights, SAS helps organizations stay compliant, confident, and competitive in a rapidly changing regulatory landscape.     Find more articles from SAS Global Enablement and Learning here. ... View more

In today’s healthcare environment—marked by escalating patient demand, constrained resources, staffing shortages, and regulatory complexities—finding smart operational solutions is not just essential, it's critical. That's where SAS comes into play: blending advanced analytics, AI, and real-time visibility to enhance healthcare operations at every level. SAS Health Care Operations & Experience Analytics Solutions offer a powerful, analytics-driven toolkit to keep everything running smoothly on the front lines.   Key Impacts of SAS on Healthcare Operations   1. Visualize and Predict Demand SAS analytics enable healthcare organizations to forecast patient demand accurately, optimizing both staffing and asset utilization. This predictive capability turns guesswork into informed decision-making.   Select any image to see a larger version. Mobile users: To view the images, select the "Full" version at the bottom of the page.   2. Accelerate Document Review with AI Medical records and documents often overwhelm clinicians and reviewers. SAS uses AI and text analytics to structure information into a shared platform, so that reviewers can work faster and smarter.     3. Optimize Workforce Management SAS operational dashboards help visualize staff capacity, costs, and even model turnover risks at department and individual levels. This insight supports proactive retention strategies and effective workforce planning.     4. Track Medication Adherence with Precision SAS enables monitoring of medication adherence across variables like condition, region, age, and compliance tier, empowering teams to target interventions where they're needed most.     5. Boost Productivity with Automation By leveraging AI-powered automation for operational workflows—including scheduling and resource allocation—SAS helps organizations streamline logistics and enhance patient experience.   6. Improve Patient Experience & Outcomes SAS unifies disparate health and non-health data into a single platform, enabling a holistic view of patients. This integration supports more personalized care and better outcome measurement   7. Optimize Contact Center Operations Contact center analytics from SAS reveal recurring topics and issues, allowing organizations to improve customer satisfaction, call resolution times, and net promoter scores.   Why It Matters Operational Accuracy: Stop scrambling to solve problems as they arise; start anticipating them. SAS enables proactive, data-led planning. Efficiency Gains: Automation and AI ease administrative burdens, letting clinicians focus on patients. Better Patient Outcomes: From improved workflows to structured data, SAS empowers care tailored to need. Innovation-Driven: Solutions like predictive maintenance and medical record automation push healthcare delivery forward.   Conclusion In a healthcare landscape where every decision affects lives, operational excellence isn't optional—it's essential. SAS equips organizations with the tools to anticipate needs, streamline workflows, and deliver better outcomes with confidence. By turning data into action, SAS helps healthcare systems not just keep up, but stay ahead.     Find more articles from SAS Global Enablement and Learning here. ... View more

In today’s intricate world of banking and finance, models sit at the heart of critical decision-making processes—ranging from credit risk assessments and capital adequacy to fraud detection and trading strategies. Let’s not forget the headache of navigating regulatory requirements. With this growing dependence comes heightened exposure to model risk—the potential for adverse outcomes due to errors in model development, implementation, or use.   Understanding Model Risk Model risk arises when a model's predictions or outputs deviate from actual outcomes, leading to potential financial losses or regulatory repercussions. This risk is amplified as institutions adopt advanced technologies like Artificial Intelligence (AI) and Machine Learning (ML), which, while powerful, can introduce biases or unforeseen errors if not meticulously managed.   The Challenges Faced by Financial Institutions Financial institutions often grapple with several challenges in model risk management (MRM):   Complexity of Modern Models: Advanced AI and ML models, though accurate, can be opaque and challenging to validate. Fragmented Governance: Disparate systems and siloed departments can lead to inconsistent model oversight. Regulatory Compliance: Adhering to evolving regulations like IFRS 9, CECL, and Basel III Endgame requires robust model validation processes. Resource Constraints: Limited resources can hinder the thorough documentation and monitoring of models.   Effective MRM is essential to maintain shareholder confidence and comply with regulations set by entities like the European Banking Authority, the US Federal Reserve, and the UK's Prudential Regulation Authority. Enter SAS Model Risk Management — a centralized, robust solution built to ensure that banks and financial institutions tackle model risk with transparency, consistency, and compliance across the entire model lifecycle.   SAS’s Approach to MRM SAS MRM offers a holistic approach to managing the model lifecycle.   Model Validation: Supports independent review and validation of models to meet supervisory guidance and business requirements. Model Organization: Facilitates the creation of model candidate assessments and comprehensive model inventories.   Select any image to see a larger version. Mobile users: To view the images, select the "Full" version at the bottom of the page.   Policy and Documentation: Establishes protocols for model-related issue tracking and documentation management.     Information Sharing: Enables the construction and dissemination of reports for effective top-down model risk reporting.   This integrated approach ensures that all model risk categories are identified, monitored, and controlled, providing clear oversight throughout the model risk lifecycle.   What Makes SAS MRM Different SAS distinguishes itself by offering:   End-to-End Governance: Comprehensive management of the model risk lifecycle, from risk identification to assessment. Automated Performance Monitoring: Automated monitoring with threshold alerts to assess model performance effectively. Governance for AI/ML Models: Intelligent automation to streamline documentation and governance for AI and machine learning models. Systematic Usage Tracking: Capture of model usage data across environments to ensure transparency and compliance. Repeatable Processes: Establishment of reliable and auditable processes for model review and validation. Centralized Information Management: A centralized model inventory supporting internal policies and procedures across all business units.   These features collectively enhance the ability to manage model risk effectively, ensuring compliance and fostering trust with stakeholders.   To Learn More To explore how SAS Model Risk Management can transform your organization's approach to model risk, visit Model Risk Management (MRM) Software | SAS. The SAS MRM e-learning course is coming soon.     Find more articles from SAS Global Enablement and Learning here. ... View more

In today’s fast-paced clinical research landscape, organizations need more than traditional data systems—they need intelligent, integrated platforms that drive speed, compliance, and collaboration. Enter the SAS Clinical Acceleration Repository: a modern solution that empowers teams to bring therapies to market faster and with greater confidence.   Let’s explore why this centralized system is becoming a critical component in modern clinical research and development strategies.   What is the SAS Clinical Acceleration Repository The repository is a collection of items available to SAS Clinical Acceleration Repository users in your organization. At its core, the SAS Clinical Acceleration Repository is an integrated platform for managing, analyzing, reporting, and reviewing clinical research information. But it's more than just a storage solution. This system combines advanced functionality with user-friendly design, providing version control, audit history, and secure access to essential clinical research assets. This platform ensures you're working with accurate, up-to-date content—without wasting time or risking noncompliance.   Behind the Interface: What’s Inside the Repository? The repository itself is hosted on a secure server, which enhances data security and minimizes the risk of data loss—far safer than relying on local storage or less protected systems. Inside, users will find a collection of containers and files. A container is an item that can contain other items, while a file contains structured and unstructured information like data, documents, and reports.   Why It Matters: Key Benefits of the SAS Clinical Acceleration Repository The SAS Clinical Acceleration Repository combines the power of SAS Viya with a centralized, secure environment to manage every aspect of clinical content. Built to streamline data analysis and support regulatory submissions, it’s designed specifically to solve the bottlenecks that often delay clinical trials. Key features include:   Faster access to integrated data and analytics End-to-end traceability and compliance-ready features Real-time collaboration tools to support distributed teams A scalable cloud platform that adapts to your needs   These features are especially valuable in ensuring transparency, data integrity, and auditability—all critical in meeting FDA, EMA, and global regulatory requirements.   Enhanced benefits of the SAS Clinical Acceleration Repository:   Integration with SAS Viya The platform is powered by SAS Viya, enabling organizations to merge advanced analytics with real-time collaboration. Whether your team includes programmers or less technical users, everyone gains secure access to data insights—improving both speed and decision-making. Cloud-Native Deployment SAS offers flexibility via cloud-native deployment options, supporting your infrastructure preferences while ensuring data security and scalability. Compliance with Industry Standards Clinical research is governed by strict regulations. SAS Clinical Acceleration Repository is designed to help organizations comply with CDISC (Clinical Data Interchange Standards Consortium), ICH (International Council for Harmonisation), and/or FDA (Food and Drug Administration) standards like the FDA Title 21 CFR Part 11, Good Clinical Practice (GCP), and other best practices. This ensures your research content stands up to scrutiny from regulators and internal reviewers alike. A Standardized, Centralized Repository With SAS Clinical Acceleration Repository, all data and documents are stored in a single, secure location—a centralized global repository that integrates with your other research systems. Accelerated Submissions By reducing manual processes and streamlining data organization, SAS helps teams get data submissions ready faster—a crucial differentiator in competitive clinical environments. A Truly Collaborative Environment SAS Clinical Acceleration Repository provides a single source of truth for regulated clinical trial operations with open integration to support third-party tools. The solution promotes collaboration across trials, phases and therapeutic areas, internally and with third parties. Research scientists and less technical users have direct access to research data, statistical results, and all other relevant clinical research content via a secure, user-friendly interface.   The Bottom Line The SAS Clinical Acceleration Repository isn’t just another content management system—it’s a transformative tool for clinical research organizations aiming to improve accuracy, transparency, and efficiency. By unifying your data, automating compliance, and fostering collaboration, SAS empowers research teams to make informed decisions faster and bring therapies to market sooner. With clear organization, intuitive navigation, and robust search functionality, users can quickly find what they need and get back to what matters: advancing science and improving patient outcomes.   To Learn More To learn more about how the SAS Clinical Acceleration can benefit you or your institution, there is an upcoming course that will explain all the solution’s features and how to use the interface. In the meantime, see SAS Help Center: SAS Clinical Acceleration Repository more details on the solution and its functionality. Please note that you must contact SAS Technical Support for an access key to be able to review this user’s guide.     Find more articles from SAS Global Enablement and Learning here. ... View more

Clinical trials are research studies that test a medical, surgical, or behavioral intervention in people. They help doctors and researchers learn more about disease and improve health care for people in the future. They contribute to ground-breaking medical advances.   Clinical trials are increasingly complex and are running faster than ever before while still being required to produce and deliver reliable results. With this complexity, it can be hard to manage all that clinical research data, regulatory requirements, and other aspects of clinical trial analysis. SAS offers the SAS Life Science Analytics Framework (LSAF) solution to manage and analyze information in a collaborative platform, streamline processes and more efficiently deliver trial results to regulatory authorities.   Additionally, you can now stay competitive in the heavily regulated word of clinical research analytics with powerful technologies from SAS, including machine learning, AI and analytics for Internet of Things (IoT).   Why choose Cloud Computing for Clinical Trial Analysis?   SAS LSAF delivers a single, open, cloud-native statistical computing environment for clinical research.   Select any image to see a larger version. Mobile users: To view the images, select the "Full" version at the bottom of the page.   Many life science organizations choose Microsoft Azure as the cloud platform for their business needs. Azure and LSAF together provide the perfect combination of cloud computing and clinical trial experience to extract valuable insights from your clinical data.   Choosing SAS on Azure will transform your clinical trial analysis and submission:   You will gain faster understanding of clinical outcomes, detect potential issues sooner, and spend more time focusing on the actual results. You will avoid delays on regulatory submissions which is key in a highly regulated environment like that of a clinical trial. You will experience increased efficiency and improved performance.   SAS Health on Azure   Incorporating cloud computing has been found crucial not just for clinical trial analytics but for SAS Health analytics overall. SAS Health on Microsoft Azure widens the scope of health analytics by simplifying health data integration, management, analytics, and storage to ultimately improve patient diagnoses, tailor patient pathways, advance clinical research, prevent diseases, and connect social determinants of health.   SAS and Microsoft Azure bring the best of analytics and cloud computing together so that health care providers can accelerate innovation and improve lives.   To Learn More   To learn more about how you can benefit from choosing SAS on Azure for your clinical trial analyses and submissions, see SAS for Transforming Clinical Trial Analysis & Submission | SAS for more details. For more details on SAS Health on Azure and its benefits, see SAS for Transforming Health Outcomes Through Analytics | SAS. Finally, to learn more about the SAS and Microsoft partnership, visit Microsoft Azure and SAS Analytics | SAS.   Find more articles from SAS Global Enablement and Learning here. ... View more

What is Stress Testing?   Stress testing involves highly complex, computer-generated simulation models that use hypothetical scenarios as their testing framework – analyzing how an organization’s balance sheet responds to specific situations. Financial institutions often face fundamental challenges in the way that they project for the assets and liabilities in their portfolios under stressed conditions. The SAS Stress Testing Solution can make the process easier. One key feature of the solution is the ability to use forecast times for which a user can perform a credit risk analysis.   How does SAS Stress Testing support analysis with forecast times?   With the SAS Stress Testing Solution, you can use the provided SAS Stress Testing scenarios to perform credit risk analysis with forecast times. A forecast time (FT) is a future time for which a credit risk analysis is performed. It is equivalent to a new base date.   SAS Stress Testing supports two approaches for handling scenarios for forecast times:   Divergent scenarios: Perform an analysis in which each scenario of a given scenario type corresponds to a single forecast time. The following are the sample divergent scenario sets: ST Adverse ScenSet 2018.12 ST Adverse ScenSet 2019.01 ST Basecase ScenSet 2018.12 ST Basecase ScenSet 2019.01 Common scenarios: Perform an analysis in which each scenario of a given scenario type corresponds to all forecast times. The following are the sample common scenario sets: ST ScenarioSet 2018.12 ST ScenarioSet 2019.01   How does SAS Risk Factor Manager come into play?   SAS Risk Factor Manger is where all the scenario sets are housed. To view the scenario sets, navigate to the Scenario Manager page within the Risk Factor Manager solution and click the Scenario Sets tab.   Select any image to see a larger version. Mobile users: To view the images, select the "Full" version at the bottom of the page.   To view which scenarios are part of a scenario set, double click one of the scenario sets from the list above, and you will see a list of all the scenarios (see screen capture below) that can be used for forecast times during credit risk analysis.     Example of a credit risk analysis that uses a forecast time   This example illustrates a credit risk analysis that uses a forecast time of one month.   If the current base date is 31DEC2018 and the FT=1 month, then:   the new base date for the FT=1 analysis would be 31JAN2019 the portfolio data is the portfolio for the 31DEC2018 base date with some possible modifications such as the addition of synthetic instruments the scenarios are added or updated for the 31JAN2019 base date. The updated scenario data might be identical to the scenario data for the current analysis base date (FT=0) but with a shock to the economic factors at the forecast time horizon (horizon=1)   If your credit risk results contain multiple horizons, you can compare the cumulative output variables (for example, ECL_Lifetime) across forecast times by using the horizon forecast time (HORIZON_FORECAST_TIME) variable. The horizon forecast time variable is the value of each horizon relative to a given forecast time's starting horizon.   Note that HORIZON_FORECAST_TIME = Horizon - Forecast Time. For example, if the Horizon=1 and the FT=1, then the HORIZON_FORECAST_TIME=0. If the Horizon=1 and the FT=2, then the HORIZON_FORECAST_TIME=-1, etc.   How do you incorporate the scenario sets for forecast time in SAS Stress Testing?   Once the user reaches the credit risk analysis portion of the SAS Stress Testing workflow, there is an option to select the Risk Scenario Sets (see screen captures below).       In the Risk Scenario Sets window (shown above), the user would select Add existing scenario, and a window then appears (see screen capture below) showing all the scenario sets that are available to choose from to use for the credit risk analysis.     The user would choose any scenario set that corresponds to the base date of the cycle and execute the script to run the credit risk analysis.   To Learn More   To learn more about how you can utilize the SAS Stress Testing solution for your institution, there is an upcoming SAS Stress Testing course that will explain all the solution’s features. In the meantime, see SAS Help Center: Welcome to SAS Stress Testing for more details on the solution including how you can use it to perform credit risk analysis with forecast times. Please note that you must contact SAS for an access key to be able to review this user’s guide.     Find more articles from SAS Global Enablement and Learning here. ... View more

There can be many business challenges related to expected credit loss of an organization when assessing credit risk. SAS Allowance for Credit Loss (ACL) enables you to address the requirements of the Current Expected Credit Loss (CECL) and International Financial Reporting Standards (IFRS 9) regulations.   With SAS ACL, you can take your IFRS9 and CECL process to a new level with a fully governed, automated workflow that includes highly efficient and precise computations.   Select any image to see a larger version. Mobile users: To view the images, select the "Full" version at the bottom of the page.   What can a user achieve with SAS ACL? Centralize the entire process Control all your models, scenarios and data in a single repository. Orchestrate the entire IFRS 9 or CECL process with flexible and traceable workflows. Get fast, precise results Get results faster with the highly efficient calculation engine and optimized processes that use in-memory processing Get full insights into results and adjust as needed Analyze the difference in provisions at the most granular level, and attribute changes to selected factors. Apply overlays with rules allocating on a granular level or directly on individual positions and keep track of all changes.     To Learn More   To learn more about how the SAS Allowance for Credit Loss solution can help you address your business challenges, visit SAS Allowance for Credit Loss | SAS. The e-learning course for the SAS ACL solution is in development and will be available for consumption in the future.     Find more articles from SAS Global Enablement and Learning here. ... View more

Clinical trials are research studies that test a medical, surgical, or behavioral intervention in people. They help doctors and researchers learn more about disease and improve health care for people in the future, and they contribute to ground-breaking medical advances.   Clinical trials are increasingly complex and running faster than ever before while still being required to produce and deliver reliable results. With this complexity, it can be hard to manage all that clinical research data among other aspects of clinical trials. SAS offers the SAS Life Science Analytics Framework (LSAF) solution to manage and analyze information in a collaborative platform, streamline processes and more efficiently deliver trial results to regulatory authorities.   What is unique about the SAS LSAF? Only SAS delivers a single, open, cloud-native statistical computing environment for clinical research with embedded analytic tools, support for data standards and optional integrated analytic applications. SAS LSAF helps users modernize and deliver new therapies to the market faster to improve health.   Some key features of the SAS LSAF solution include:   Faster time to market and cost containment Improved efficiency and reduced errors in data aggregation and preparation Maximum value from existing or new analytics applications Broader access to programming Rigorous statistical analysis and regulatory controls Expanded access to and collaboration with clinical data Solid framework for traditional and emerging trial designs   What are some ways that SAS LSAF improves speed and efficiency of analyzing and reporting clinical trial data?   LSAF drives global collaboration among internal team members, consultants, contractors and development partners LSAF fully embraces open source, enabling hiring managers to expand their hiring pool by giving users the flexibility to program in SAS, R, or Python.   Select any image to see a larger version. Mobile users: To view the images, select the "Full" version at the bottom of the page.   LSAF expands information management by providing a fully integrated environment that spans from operational data systems (such as eCRF), electronic health records, sensors and wearables, omics data, biomarker data, etc., through standardization, analysis and reporting, and post-approval meta-analysis. In this way, LSAF saves users time on operational data management and gives them more time to explore, monitor data quality, and execute advanced analytics and statistics.     To Learn More   To learn more about how the SAS Life Science Analytics Framework can help you improve your clinical trial processes, visit Pharma Analytics | SAS Life Science Analytics Framework | SAS. Course curriculum for the LSAF solution is in development and will be available for consumption in the future.   Find more articles from SAS Global Enablement and Learning here. ... View more

Financial institutions face fundamental challenges in the way that they project for the assets and liabilities in their portfolios under stressed conditions. There are two primary types of stress tests that financial institutions must perform:   Regulatory stress tests – stress tests that are required by regulatory agencies. These include the Federal Reserve’s Comprehensive Capital Analysis and Review (CCAR) regulatory framework, the European Banking Authority’s (EBA) regulatory framework for Enterprise-Wide Stress Testing (EWST), and the Basel II Internal Capital Adequacy Assessment Process (ICAAP) regulatory framework.   Business-as-usual stress tests - business-as-usual stress tests are internal stress tests that can be used to plan for and quantify risks resulting from strategic changes to an institution’s portfolio over time.   Both of these types of stress tests require institutions to calculate potential credit losses and create loss reserves.   The purpose of this post is to discuss what the SAS Stress Testing solution offers and to discuss two of the many tasks that users can perform in the solution.   What is a cycle in the SAS Stress Testing solution? In SAS Stress Testing, a cycle represents the stress testing activities that are conducted for a given time period under a specific set of risk scenarios. The workflow, ST_Cycle, which is provided as a sample in the SAS Stress Testing solution consists of the following tasks:   Data processing Business projection analysis Portfolio projection analysis Credit risk analysis Attribution analysis Sign-off   The ST_Cycle workflow:   Select any image to see a larger version. Mobile users: To view the images, select the "Full" version at the bottom of the page.   Credit Risk Analysis and preceding tasks in the SAS Stress Testing workflow  SAS Stress Testing enables you to conduct regular stress tests on an organization’s assets and liabilities. These stress tests can be for the purposes of internal auditing, external auditing, or to meet regulatory reporting requirements. Before reaching the credit risk analysis task in the workflow, users must complete the following preceding tasks (each task includes its own set of subtasks):   Data processing Analysis data is prepared for use in the credit risk analysis. A user can run data quality checks on the analysis data and review the data quality results in the Data Quality Report. Business projection analysis A user creates the Breakeven Point (BEP) and then updates an existing BEP with new projections. A user then reviews the BEP results in a report. Portfolio projection analysis A user generates front books for different portfolio and allocation model combinations. Credit risk analysis This task contains two sub-tasks: Run Attribution Analysis and Review Attribution Analysis Results. During the Run Attribution Analysis sub-task, a user runs attribution analysis to see what factors contributed to period-over-period changes in expected credit loss. Then, during the Review Attribution Analysis Results sub-task, a user reviews the attribution analysis results in the Management Report. The user can either go to the next workflow task to sign off on the results or go back to the Run Attribution Analysis sub-task to run the attribution analysis again. In any of the previous tasks, a user can also revert back to the task if not satisfied with the results.   SAS Stress Testing includes the following credit risk stress testing scenarios:   Common scenarios:   ST ScenarioSet 2018.12 ST ScenarioSet 2019.01   Divergent scenarios:   ST Basecase ScenSet 2018.12 ST Adverse ScenSet 2018.12 ST Basecase ScenSet 2019.01 ST Adverse ScenSet 2019.01   To Learn More To learn more about how you can utilize the SAS Stress Testing solution for your institution, there is an upcoming SAS Stress Testing course that will explain all the solution’s features. In the meantime, see SAS Help Center: Welcome to SAS Stress Testing. Please note that you must contact SAS for an access key to be able to review this user’s guide.     Find more articles from SAS Global Enablement and Learning here. ... View more

The purpose of this blog is to discuss how you can use SAS Risk Engine to perform risk analyses through the use of risk pipelines. You can use risk pipelines to create the risk environment, score counterparties, evaluate portfolios, and explore results. Furthermore, with the SAS Risk Engine interface, you can complete many of the same activities that can be accomplished through code. You can configure, execute, and explore the results of a Risk environment all from a browser in a low-code environment.   Some key business objects to keep in mind Methods and maps are objects that you reference in your pipeline. Evaluation and scoring methods are created on the Risk Methods page. Maps are created from the Maps page (see screen capture below that shows the available pages in the user interface).     In the pipeline, you use the following action nodes to add these programs to the pipeline.     A method program consists of a set of computational statements that the risk engine uses to compute one or more output variables for specific types of objects in your portfolio data.   Risk method maps show the relationship between risk methods and specific types of objects in your portfolio data.   What is the process of using a risk pipeline for your analyses?   You follow a step-by-step process in the SAS Risk Engine user interface that is similar to the one used when using SAS Risk Engine through code.     You begin with a risk pipeline, and you add functionality to the pipeline by adding notes to it. Keep in mind that whether you use SAS Risk Engine through code or the user interface, several input tables are required to perform the risk analyses. These input tables need to be placed in memory in Cloud Analytic Serves (CAS) before they can be used in a pipeline. Whichever nodes you add to the pipeline can then be configured. See screen capture below that shows a successfully run risk pipeline containing nodes.     The first step, creating a risk environment, is automatically created for you during pipeline execution, so there are no actions you need to take to complete this step.   After you configure a pipeline, you just need to submit the run to generate analysis results. The risk engine performs a few prerequisite tasks and then runs all the nodes you’ve configured in the pipeline sequentially.   You can then review the results for the pipeline itself and for each node. You can explore tables in SAS Visual Analytics and in SAS studio. Furthermore, depending on the configuration of the pipeline, results might be available for review in SAS Risk Explorer.   To Learn More For more details on SAS Risk Engine and what it has to offer, visit the e-course available through the SAS Learning Subscription: Course: Using the SAS® Risk Engine Interface. ... View more

In the first two parts of this series (links below), we discussed the user’s ability to customize the solution’s screen definition files and how to create and edit a groovy function. The purpose of this article is to continue to discuss SAS Risk Governance Framework and the customizable user experience that it offers.   To review, risk governance describes the rules, conventions, processes, and mechanisms by which decisions about risks are taken and implemented. It is inevitable that institutions experience risk (whether planned or unplanned) as a result of their business processes, and with effective risk governance, organizations can benefit from the changes that risk can bring while minimizing the negative consequences.   Customizing New Business Objects SAS Risk Governance Framework enables users to design personalized business tasks by leveraging custom business objects. As a reminder, some examples of business objects within the SAS Risk Stratum solution are Rule Sets, Models, Analysis Runs, Master Risk Scenarios, etc. If a specific business object is not available or provided in SAS Risk Stratum, users can generate their own custom business objects.   Adding a Business Object to the Navigation Menu After performing multiple prior steps as a part of incorporating custom business objects in SAS Risk Governance Framework, the final step is for the user to add the new business object to the main navigation menu in the user interface (UI) within SAS Risk Governance Framework. The user can then restart the SAS Risk Governance Framework Web Application Server and then verify that the new business object appears in the main navigation menu of the UI.   Outlined below is a summary of the steps needed to add the business object to the navigation menu.   First, the user navigates to DAVTree.bat in the file explorer to open the DAVTree application and run as an administrator. After signing in with the necessary credentials, the user navigates to the following pathway within the DAVTree application from the menu on the left-hand side (see screen capture below). sasdav > Products > SASRiskGovernanceFramework > RiskGovFrwkMid-Tier > rmc > Config > Menus   Select any image to see a larger version. Mobile users: To view the images, select the "Full" version at the bottom of the page.   In the Config > Menus tab, the user accesses the MainNavigation.xml file by right-clicking and selecting Edit. Then, from the file explorer, the user accesses the code from the MainNavigation.xml file using this pathway: 😧 > Student Files RGF > customobject > MainNav. The code from lines 68-77 needs to be copied from the file (see screen capture below).     The code from above is then pasted in the DAVTree Edit file window between the Analysis Run and Results sections:     The next steps are to restart the SAS Risk Governance Framework Web Application Server to apply the changes and then to verify that the new business object appears in the navigation menu of the UI:     To Learn More As we can see, the SAS Risk Governance Framework UI is customizable in many ways. For more details on the exact steps required to adding a business object to the navigation menu along with other customizations, visit the e-course available through the SAS Learning Subscription: Course: SAS® Risk Governance Framework: Solution Overview.   Prior articles in this series:   SAS Risk Governance and a Customizable User Experience SAS Risk Governance and a Customizable User Experience Part 2     ... View more

The purpose of this blog is to introduce the concept of Stress Testing and why it is important to perform for institutions such as banks. Stress Testing is a computer-simulated technique that is used to test the resilience of an institution and investment portfolios to see how they fare in drastic economic or financial scenarios. Stress Testing is crucial for an institution to perform because it helps gauge investment risk and the adequacy of the institution’s assets as well as evaluate internal processes. This process will then allow the institution to develop strategies necessary to mitigate against any possible losses.   There are regulations in place that require banks to carry out various stress-test scenarios and report on their internal procedures for managing capital and risk. Banks continue to face new regulatory, internal, and market challenges. SAS provides a solution that banks can use to carry out the required stress testing procedures and address the persistent challenges they experience.   SAS Solution for Stress Testing   SAS Solution for Stress Testing is a user-friendly, web-based solution that enables the user to perform stress testing activities and facilitates the capture and management of stress testing-related information. The solution provides the user an opportunity to simulate their business in a simple way while offering a comprehensive (covering all risk types), detailed, fast, and flexible environment.   SAS Solution for Stress Testing contains built-in workflows and user interfaces that can be easily customized by business users to meet the specific needs of their organization. The solution allows users to centrally orchestrate the entire stress testing process and foster collaboration across the organization. The solution also provides powerful, built-in dashboarding and reporting capabilities using SAS Visual Analytics, which is a component of the solution.   The Stress Testing Process Flow using SAS Solution for Stress Testing   SAS Solution for Stress Testing acknowledges that there are different types of stress tests that fall under the primary categories of regulatory stress tests (imposed by regulatory agencies) and business-as-usual stress tests (self-imposed internal stress tests). Therefore, the solution follows practices and approaches for different regulatory stress testing frameworks and offers financial institutions a tailored user interface (UI) for these different frameworks: Enterprise-Wide Stress Testing (EWST), Internal Capital Adequacy Assessment Process (ICAAP), and Comprehensive Capital Analysis and Review (CCAR).   Select any image to see a larger version. Mobile users: To view the images, select the "Full" version at the bottom of the page.   For any of the above stress testing frameworks, SAS Solution for Stress Testing offers menu-driven dashboards that make it easy to manage multiple stress tests, incorporate a range of scenarios and portfolio assumptions, and execute the stress tests quickly. One great feature of this menu-driven dashboard is the workflow which can be used to define tasks in the cycle for a specific base date (or time period), coordinate the participation of different users throughout the entire process, and generate reports to summarize the results of the workflow activities. The sample cycles that the solution offers are used to centralize access to data, models, and scenarios for a specific time period.   EWST Process Flow Example   The screen capture below shows an example workflow diagram for SAS Solution for Stress Testing: EWST. The workflow depicts the different stages associated with the stress testing cycle called the EWST Cycle.     For the cycle to be completed, it must be initiated, data must be prepared, assumptions must be configured, analysis must be performed and reviewed, adjustments and allocations must be made, and the cycle must be signed-off.   The customizable feature of the solution allows users to customize or tailor the workflow to meet their organization’s specific business structure and process management needs.   To Learn More   This is just one of the many different aspects of the SAS Solution for Stress Testing. To learn more about SAS Solution for Stress Testing, visit the e-course available through the SAS Learning Subscription: Course: SAS® Solution for Stress Testing: Solution Overview ... View more

In part 1 of this blog, we discussed the basics of risk governance, introduced the SAS Risk Governance Framework solution within SAS Risk Stratum and discussed the user’s ability to customize the solution’s screen definition files. The purpose of this blog is to continue to discuss SAS Risk Governance Framework and the customizable user experience that it offers.   To review, risk governance describes the rules, conventions, processes, and mechanisms by which decisions about risks are taken and implemented. It is inevitable that institutions experience risk (whether planned or unplanned) as a result of their business processes, and with effective risk governance, organizations can benefit from the changes that risk can bring while minimizing the negative consequences.   Groovy Function Editor   Groovy is a programming language that runs on the Java platform but also has features inspired by other programming languages such as Python, Ruby, and Smalltalk. Groovy is simple to understand and maintain, and it integrates seamlessly with all existing Java classes and libraries.   The SAS Risk solution comes with an extensive list of pre-loaded Groovy functions with prewritten code in the Groovy programming language. The solution’s Groovy Function Editor is a user interface (UI) component that enables the user to view or modify the existing Groovy functions or create and test new custom functions that use Groovy. Groovy functions have many uses. They can be used in expressions, to define configurations, as a filter criterion, or to specify UI behavior.   Select any image to see a larger version. Mobile users: To view the images, select the "Full" version at the bottom of the page.   Creating a Groovy Function   As previously mentioned, the solution allows users to use Groovy programming language to create their own Groovy function as well as modify it. Creating a Groovy function lies on the Administration page of the SAS Risk Stratum solution on the Groovy Function Editor tab. On that tab, there is an option to Create new Groovy Function once the Scope is set to rmc-sampl (note: to view all of the pre-loaded Groovy functions, Scope should be set to rmc).   Users can then name their new function before clicking Create. Then, the window displays the Java syntax that defines a custom function. Below is an example of a function that was newly created called DemoTest. You can see the code displayed on the left (highlighted in green), and on the right, the function can be tested by entering an argument and clicking Test, which then displays the results (highlighted in red).     Editing a Groovy Function   Not only can users create a new Groovy function, but they can also edit the function. The screen capture below is a zoomed in view of the code from the screen capture above.     The original code above can be modified to edit the function. In the screen capture below, the highlighted areas in red are where the changes are made.     After making the above changes, there are now two argument fields on the right-hand side rather than one. Once each argument is entered, they are displayed in the Results window at the bottom right.     To Learn More   SAS Risk Governance Framework includes documentation on all the pre-loaded Groovy functions found within a risk solution. This documentation is available on the Function Documentation subtab within the Documentation tab on the Administration page.     To learn more about SAS Risk Governance Framework, visit the e-course available through the SAS Learning Subscription: Course: SAS® Risk Governance Framework: Solution Overview. ... View more