SAS Support Communities

Contact: Her Excellency Dr Mubaraka Ibrahim    Country: Dubai   Award Category: Community Uplift   Tell how you've used SAS to have a positive impact on your community.  We have been working on incorporating safe and responsible AI best practices into the AI work being done at Emirates Health Services, the federal government authority in the UAE that manages the health and wellness of a large UAE population.    While developing several AI / ML programs on the SAS platform implemented at EHS, we have had several learnings, successes, and challenges to incorporate the Safe AI guidelines from across the globe.    As Artificial Intelligence transforms healthcare and revolutionizes clinical decision-making processes in the UAE, AI-powered solutions augment clinicians' decision-making abilities, improve healthcare operations, and navigate public health systems from care to wellness. However, with this transformative potential come significant ethical challenges, such as issues of bias, transparency, accountability, and privacy.    These challenges accelerated our work on responsible AI, which seeks to ensure that AI systems are developed and deployed in a manner that is ethical, fair, transparent, accountable, and beneficial to all users.   What SAS products are you using and how are you using them?  SAS Viya, SAS Data Management, SAS Visual Investigator    What was your most surprising discovery about your work?  Despite an apparent agreement globally that AI should be ‘ethical’, there is still a need for a debate about both what constitutes ‘ethical AI’ in healthcare and which ethical requirements, technical standards and best practices are needed for its realization.     ... View more

Contact: Turk Telecom    Country: Turkey   Award Category: Innovative Problem Solver   Tell us about the business problem you were trying to solve.Required to answer. The main business problem resolved around optimizing data processing and analytics capabilities while maintaining scalability and performance for large datasets. For Turk Telekom, this includes:   • Handling Massive Data Volumes: As a telecommunications company, Turk Telekom manages vast amounts of customer data, network performance data, billing information, and more. They need a system that can process, analyze, and make real-time decisions with such large datasets. • Real-Time Insights: The demand for real-time analytics, such as marketing optimization, fraud detection, customer behavior analysis, etc., requires a highly responsive and fast system. With the transition to Single Store, they were looking to ensure faster data processing and real-time analytics capabilities. • Data Consolidation & Modernization: With multiple data silos, there’s a need to streamline access, storage, and querying of data. They were looking to move to a more unified platform (SAS Viya with Single Store) to improve operational efficiency, reduce system complexity, and lower overall costs. • Scalability for Future Growth: As Turk Telekom grows; they required systems that can scale without losing performance. The shift from Cloudera to Single Store, along with the upgrade of SAS Viya, achieved preparing the organization for future data challenges and providing a scalable solution.   What SAS products did you use and how did you use them? In the previous state (SAS Viya 3.5 with Cloudera), Turk Telekom leveraged several SAS Viya components for analytics and decision support, such as: • SAS Visual Analytics (VA): Used for visualizing data and generating interactive reports for business users. This helps with insights into customer trends, decision process, marketing activities and operational efficiency. • SAS Data Preparation (SDP): Used for preparing large datasets, cleaning them, and transforming them into a usable format for analytics and machine learning. • SAS Model Studio and SAS Viya Machine Learning: These products would have been used for developing predictive models for customer behaviors, fraud detection, and other key business operations. After the upgrade to SAS Viya 4 with Single Store: • SAS Viya 4 provided an upgraded, more flexible, and cloud-native platform that integrates better with modern technologies. It would improve collaboration across teams, offer more advanced analytics capabilities, and support faster model training and deployment. • SAS Data Management tools also integrated with Single Store, providing a more seamless and scalable data storage and processing environment.   What were the results or outcomes? By upgrading to SAS Viya 4 and migrating to Single Store, Turkish Telekom gain several key benefits: • Improved Performance and Scalability: The shift from Cloudera to Single Store enabled significantly faster query performance, especially for large-scale datasets. Single Store’s architecture is designed for high-speed, real-time analytics, making it easier to scale as the business grows. • Better Real-Time Analytics: With SAS Viya’s capabilities in combination with Single Store’s real-time data processing, Turk Telekom experienced more timely insights and improved decision-making capabilities. • Streamlined Data Management: Consolidating various data sources into a single platform reduced the complexity of managing multiple systems, helping Turk Telekom save time, resources, and reduce the risk of data silos. • Cost Efficiency: The cloud-native architecture of SAS Viya 4 combined with Single Store allowed Turk Telekom to optimize infrastructure costs. They leveraged cloud scalability and pay for resources as needed, which provided significant long-term savings.   Why is this approach innovative? This approach is innovative for several reasons: • Seamless Integration of Analytics and Database Solutions: Combining SAS Viya’s advanced analytics with Single Store's high-performance, real-time data processing provides an integrated, end-to-end solution. This unified approach helps organizations move away from fragmented, siloed systems, enabling better cross-functional collaboration and improved operational efficiency. • Real-Time, Scalable Cloud-Based Architecture: By upgrading to SAS Viya 4 and utilizing Single Store, Turkish Telekom is adopting a more modern, cloud-native architecture that supports real-time data analysis and scaling. The flexibility of cloud solutions, combined with the speed and efficiency of Single Store, makes this a forward-looking approach for a growing organization. • Focus on Speed and Real-Time Decision Making: The emphasis on faster analytics and real-time insights allows Turkish Telekom to be more responsive to changing customer demands, network issues, or market conditions. This could give them a competitive advantage in terms of customer experience and operational agility. • Innovation in Data Infrastructure: By migrating from Cloudera (a more traditional data storage/processing system) to Single Store, Turk Telekom is embracing a modern database solution that’s purpose-built for modern analytics needs. This shows an innovative shift towards adopting cutting-edge technology for better performance and data handling. In essence, Turk Telekom is setting itself up for future success by adopting a more scalable, efficient, and responsive technology stack that allows them to harness the full potential of their data. This innovative approach enables smarter, faster decision-making while optimizing operational costs.   ... View more

Rossmann   Contact: Adam Marchel   Country:  Poland   Award Category: Innovative Problem Solver   Tell us about the business problem you were trying to solve. We needed to determine the optimal size for each delivery to our drugstores. This information is crucial for planning the details of our replenishment strategy.   What SAS products did you use and how did you use them? We leveraged the power of SAS Viya Optimization and CAS to tackle this challenge. CAS enabled us to process large volumes of data swiftly and efficiently. Viya Optimization played a crucial role in tackling the most challenging tasks.   What were the results or outcomes? We achieved full automation of a previously time-consuming business process. This new process provides detailed insights on the number of distinct products to send to our drugstores over a mid-term horizon, factoring in specific business considerations like promotions, new store openings, various configurations, parameters and lots of constraints. Additionally, it aligns with our goal of minimizing stockouts.   Why is this approach innovative? By utilizing advanced optimization tools in SAS Viya, we were able to solve a highly complex problem that previously relied on less sophisticated tools like Excel. This new approach allows us to simulate multiple scenarios and select the one that best meets our requirements. ... View more

Contact: Lokanatha Settipalli    Country: UAE   Award Category: Innovative Problem Solver   Tell us about the business problem you were trying to solve.   The adoption of SAS Viya significantly enhanced our ability to process large volumes of data. Previously, we faced challenges in analyzing vast amounts of transactional and aggregated data. SAS Viya's in-memory processing engine allowed us to overcome these big data analytics challenges, enabling us to process data and gain insights effectively. This capability has been crucial in empowering business leads to monitor customer performance trends and make data-driven decisions.    What SAS products did you use and how did you use them?  At DPWORLD, SAS has been instrumental in our successful implementation of data analytics to achieve business objectives and strategy within the Commercial and Operations. As an Analytics Manager I have witnessed firsthand how we began by leveraging SAS Visual Analytics and Visual Statistics to answer complex business queries with a strong foundation in statistics and analytics. These analytical modules enabled us to uncover insights at unprecedented speeds and find new ways to look at our business problems.     As our analytics capabilities matured, we upgraded to SAS Visual Data Mining and Machine Learning (VDMML) and SAS Visual Forecast (VF), allowing us to implement all four pillars of analytics: descriptive, diagnostic, predictive, and prescriptive. SAS Visual Forecast proved particularly valuable in strengthening our forecasting use cases, blending various models to predict demand accurately by integrating multiple input variables.    The adoption of SAS Viya significantly enhanced our ability to process large volumes of data. Previously, we faced challenges in analyzing vast amounts of transactional and aggregated data. SAS Viya's in-memory processing engine allowed us to overcome these big data analytics challenges, enabling us to process data and gain insights effectively. This capability has been crucial in empowering business leads to monitor customer performance trends and make data-driven decisions.    What were the results or outcomes?  The adoption of SAS Viya significantly enhanced our ability to process large volumes of data. Previously, we faced challenges in analyzing vast amounts of transactional and aggregated data. SAS Viya's in-memory processing engine allowed us to overcome these big data analytics challenges, enabling us to process data and gain insights effectively. This capability has been crucial in empowering business leads to monitor customer performance trends and make data-driven decisions.    Why is this approach innovative?  The impact of SAS on our analytics capabilities has been transformative. It has empowered our business users as citizen data scientists and statisticians to solve previously unfeasible business problems by removing barriers created by data sizes, data diversity, and computational bottlenecks. This has led to greater productivity, faster insights, and more creative solutions to our most complex problems, ultimately driving our organization's success in leveraging data analytics for business growth.  ... View more

Contact: Amresh Kumar    Country: India   Award Category: Community Uplift   Tell how you've used SAS to have a positive impact on your community.  I've leveraged SAS to create a positive impact on my community through several initiatives focused on accessible and equitable healthcare system. By harnessing the power of historical data and predictive modeling, I've been able to:    - Increase access to affordable health insurance: Using SAS, I developed models that analyze historical data to predict future risks and identify individuals who may face challenges in securing affordable health insurance. This allows for proactive outreach and personalized guidance, connecting people with suitable plans and resources.    - Ensure fair pricing and transparency: My SAS models help detect anomalies and potential biases in pricing structures, ensuring fairness and transparency in health insurance costs. This helps protect individuals from discriminatory practices and promotes equitable access to healthcare.    - Provide fast and efficient claims settlement: By streamlining and automating the claims process with SAS, I've significantly reduced processing times. This ensures that individuals receive timely reimbursements and reduces the financial burden during critical moments.    These initiatives demonstrate how SAS can be used to address real-world challenges and improve the well-being of a community. By combining data analysis with a focus on social impact, I'm proud to contribute to a more equitable and accessible healthcare system.    What SAS products are you using and how are you using them?  SAS Studio and SAS Viya    What was your most surprising discovery about your work?  That's an interesting question! It really made me reflect on my journey with SAS.    Honestly, the most surprising discovery wasn't about the technical capabilities of SAS itself (though those are impressive!). It was more about the human element.    I was surprised by how much my work could positively impact real people in my community. Seeing how something I built with SAS could help someone access healthcare they couldn't previously afford, or how it could alleviate the stress of a slow claims process during a difficult time – that was truly eye-opening.    It really brought home the idea that data and technology, when used thoughtfully, can be powerful tools for social good. That's something I'll carry with me going forward.        ... View more

Contact: Amresh Kumar   Country:  India   Award Category:  Innovative Problem Solver     Tell us about the business problem you were trying to solve.  We needed a model to help us project business renewals. This would allow us to forecast future revenue and identify areas for improvement in our customer retention strategy.    What SAS products did you use and how did you use them? SAS Studio and SAS Visual Analytics for dashboarding    What were the results or outcomes?  We needed a reliable way to forecast future revenue from renewals. To address this, we built a predictive model that analyzes historical renewal trends. This model incorporates key parameters such customer churn rate, policy duration, sourcing channel, age etc. and pricing revision in case for any products. successfully built it    By leveraging this model - Accurately forecast future revenue with the accuracy of 98%: This allows for better financial planning and resource allocation also Identify trends and patterns by understanding the factors driving renewals, have proactively address the potential issues and improve our overall customer retention strategy.  Set realistic KPIs for the sales team using Data-driven targets help motivate the sales team and ensure alignment with company goals. This model has become a crucial tool for our business, enabling data-driven decision-making and improved sales performance.    Why is this approach innovative?  This approach to revenue projection is innovative for several reasons:    A key reason was high accuracy: Achieving 98% accuracy in renewal forecasting is significant. Many traditional methods rely on simpler calculations or gut feelings, which can be less reliable.    Data-driven insights: By incorporating key parameters and analyzing historical trends, the model provides valuable insights into customer behavior and renewal drivers. This allows for a deeper understanding of the business and more effective strategies for improvement.    Actionable KPIs: The model's ability to define data-driven KPIs for the sales team is a key innovation. This ensures targets are realistic and aligned with overall business objectives, leading to improved sales performance and motivation.    Proactive approach: Instead of simply reacting to renewal outcomes, this model enables a proactive approach to customer retention. By identifying potential churn risks early on, businesses can take steps to mitigate them and improve customer satisfaction.    ... View more

North Carolina Agricultural and Technical State University   Contact: Harmandeep Sharma   Country:  USA   Award Category: Community Uplift   Tell how you've used SAS to have a positive impact on your community. As a Research Assistant Professor in the College of Agriculture and Environmental Sciences (CAES) at North Carolina A&T State University (NCA&T), I am dedicated to harnessing digital agriculture tools—such as sensors, drones, and machine learning technologies—to refine resource management for specialty crops (hot peppers, fresh-market tomatoes, and dual-purpose hemp) in North Carolina. My focus is particularly aimed at enhancing the capabilities of small-scale farmers through the integration of precision technologies. A critical component of my work involves extensive use of data analytics, driven by real-time data collected from various digital tools. In this context, SAS products play a pivotal role in my research, teaching, and outreach initiatives, all directed toward uplifting underserved communities and limited-resource farmers in North Carolina. Within our research framework, my students and I use SAS Viya to analyze drone and sensor data to develop predictive models. Our findings are regularly presented at scientific conferences and farmer outreach events, significantly advancing knowledge transfer in sustainable crop production and providing direct benefits to small-scale farmers. Dr. Sharma, who is instrumental in supporting agricultural analytics at our college, leads the Ag Analytics platform. This initiative has successfully secured $1 million in recurring funds from the North Carolina General Assembly and is a collaborative effort among North Carolina A&T State University, NC State University, and SAS, aimed at bolstering agricultural research and facilitating statewide dissemination of our findings (https:/caes.news/sas-ai-platform-to-help-n-c-at-ncsu-take-research-to-the-people/). In terms of outreach, Dr. Sharma has orchestrated numerous workshops for graduate students, faculty, and extension agents, empowering them to effectively analyze their datasets and translate raw data into actionable insights. Beyond these technical contributions, Dr. Sharma is a dedicated educator, involved in teaching key graduate-level courses such as Crop Ecology (NARS 604) and Experimental Methods in Research (AGRI 604), integral to the academic structure of the College. Furthermore, under Dr. Sharma’s leadership, a team of students from NC A&T participated in the SAS Hackathon. Our team, named "Esse Quam Videri"—North Carolina’s state motto meaning "To be rather than to seem"—focused on analyzing and addressing gender and racial disparities among small-scale rural farmers (https://caes.news/using-data-to-make-a-difference-at-team-competes-in-sas-hackathon/). By utilizing the SAS Data Maker platform, we aimed to develop open-source datasets that protect sensitive information yet provide invaluable resources for policymakers dedicated to supporting female and minority farm owners. This innovative, multidisciplinary project not only achieved finalist recognition at SAS but is also being considered as a potential framework for the enhancement of agricultural statistical reporting. Through these varied initiatives, SAS technology has empowered me to assist small-scale farmers in making informed decisions and advocating against disparities, significantly impacting the lives of small, limited-resource, and minority farmers who are crucial to the sustainability of North Carolina’s agricultural landscape.   What SAS products are you using and how are you using them? As a Research Assistant Professor, I have extensively utilized various SAS products to enhance the scope and efficacy of my research projects. These include SAS Visual Analytics, SAS Model Studio on the SAS Viya platform, and SAS Data Maker. My research focuses on understanding plant responses to varied agricultural inputs, such as fertilizers and irrigation, across multiple spatial scales—from individual leaves to entire plant communities. To facilitate this research, I employ a diverse array of tools, including portable infrared gas analyzer leaf chambers and drones equipped with multispectral and thermal imaging sensors. SAS Viya is frequently used to manage the substantial datasets collected from these tools, which record data on an hourly and daily basis. Data integration is further enhanced by artificial intelligence through SAS Model Studio, where I create and compare modeling pipelines. This strategic application of SAS technology significantly expedites knowledge transfer, enhancing productivity and sustainability among farmers. Additionally, during this year's SAS Hackathon participation, our team employed novel data anonymization techniques and synthetic data generation tools within SAS Viya and SAS Data Maker to address gender and racial disparities among small rural farms. Moreover, as the College's technical lead for the Ag Analytics platform, I have organized several workshops centered on SAS technologies. These workshops cover topics such as SAS Visual Analytics, SAS Visual Statistics, and advanced forecasting and modeling techniques using SAS Viya and SAS Model Studio. I am the first faculty member from NC A&T to receive the prestigious SAS HBCU+ Fellowship award, and one of only four recipients nationwide in 2024 (https://blogs.sas.com/content/sascom/2024/02/07/passion-in-practice-how-sas-hbcu-fellows-are-shaping-future-tech-leaders/). I have integrated SAS Viya into one of my graduate-level courses, Experimental Methods in Research (AGRI 604), which is vital to the graduate program at CAES at NC A&T. Through these educational initiatives, I aim to empower students, faculty, and extension agents with the skills necessary to transform complex datasets into actionable insights, furthering the impact of digital agriculture on sustainable farming practices.   What was your most surprising discovery about your work? One of the most enlightening discoveries in my research came from integrating advanced SAS products, such as SAS Viya and SAS Model Studio, into the Ag Analytics platform. This integration enabled real-time visualization and analysis of massive datasets collected via sensors and drones. Remarkably, I was able to efficiently create and compare multiple predictive models, selecting the best-performing model (champion model) within a few months. This capability not only facilitated the dissemination of my findings at multiple scientific conferences and farmer outreach events but also supported applications for prestigious research grants, including the USDA AFRI Data Science for Food and Agriculture Systems program. Furthermore, during our participation in the SAS Hackathon, our project focused on the systemic factors leading to gender and racial disparities, which often result in inequitable outcomes for small rural farms. To our surprise, SAS Data Maker enabled the examination of granular-level patterns and trends without compromising sensitive personal information, thereby promoting an environment conducive to equity-driven decision-making. The integration of SAS Data Maker provided a robust framework for generating high-quality synthetic data that closely mirrors real-world scenarios, enhancing the reliability of our findings. Therefore, by incorporating SAS products like SAS Viya, Model Studio, and Data Maker into my projects, I have not only improved the accuracy and efficacy of our predictive models but also enhanced my ability to address critical social issues within the agricultural sector. The success in using data science to reveal and potentially mitigate disparities in farming underscores the broader implications of my team’s work. It illustrates a path forward where advanced analytics and ethical data practices can coalesce to create more just and sustainable agricultural systems. Ultimately, these efforts reflect both my commitment and that of NCA&T to leveraging cutting-edge technology to foster inclusivity and resilience in farming communities, setting a new standard for how agricultural data is utilized to benefit society as a whole. ... View more

EY ifb   Contact: Markus Weick   Country:  Germany   Award Category: SAS Analytics Explorers Advocate   How has SAS Analytics Explorers benefited your career? As a Risk Management Professional turned IT-Consultant I found SAS Analytics Explorers especially helpful for the following aspects - Support my lifelong learning - Shape my personal brand and showing my commitment - Extending my network   SAS Analytics Explorers is the starting point of my daily SAS activities. It provides me with wider SAS Knowledge and with the resources (points) for certifications. Together with activities in the SAS support communities I can grow contacts with other SAS users which have already lead to approaches for common work. Secondary effects of the SAS Analytics Explorers offers are the showcasing of SAS compentencies via certifications, the SAS communities profile, Credly and LinkedIn, which helped me in getting project assignments externally as well a internally. For example I was assigned the task of supporting our new joiners in learning SAS. And I think, that for instance the visibility due to my active involvement with the SAS Analytics Explorers helped with my promotion last year.     (And I have to admit, that it’s fun to get points for challenges about yourself. 😊)   What is your favorite challenge you've completed in the program? There are quite a few candidates for the favorite role. Since I joined in November ’21 I participated in nearly all of the challenges and a few weeks ago I reached the 100.000 mark.     As can be seen when comparing my ranking in the different categories I especially like the whole Education category.     If I had to select a single one as favorite, it’s still the „Become an Analytics Thought Leader“ series, which prepared the article by Jack Phillips, Jason Larson (https://iianalytics.com/community/blog/six-must-have-mindsets-to-be-a-world-class-data-and-analytics-leader). It‘s setting the SAS offers into a wider context. But I also like the programming challenges, especially if they have a fun component.       How have you shared the program with others at your organization? I use several ways to share the program with others, not only at my organization. I love to wear my SAS Analytics Explorers hoodie in SAS related MS Teams meetings. It sometimes leads to explaining about SAS Analytics Explorers. Another way is the introductionary SAS training for our new joiners, where I present SAS Analytics Explorers within the SAS context.     I also use the hashtag #SASAnalyticsExplorers when sharing SAS posts in the social networks. In the SAS Suppurt Communities SAS Analytics Explorers are part of my profile.     Furthermore I am talking to SAS users outside my organization, clients for example, who I have referred successfully to the SAS Analytics Explorers program. Especially proud I am of last years award winner, a client of mine, whom I introduced to SAS Analytics Explorers:   So we could fly to last years SAS Innovate together. As one of the team members said: „Markus, you are living the SAS Analytics Explorers“.   What SAS products do you use? SAS Enterprise Guide and SAS Studio ... View more

Contact: Darço Akkaranfil    Country: Türkiye   Award Category: Innovative Problem Solver   Tell us about the business problem you were trying to solve. Burgan Bank was undergoing a digital transformation to shift towards retail banking, and faced a pressing need to modernize legacy systems and processes within just nine months. The bank’s primary challenges were: ● Enhancing customer engagement in a highly competitive market ● Mitigating risk related to credit allocation, collections, and fraud prevention ● Managing multiple parallel projects with governance and prioritization challenges. These issues were slowing the bank’s ability to scale and respond to evolving market demands. What SAS products did you use and how did you use them? Burgan Bank used several SAS solutions to address these challenges: ● SAS Intelligent Decisioning is the key product in enabling decision flows and rule-based approaches for all projects. ● SAS Visual Analytics was employed for data visualization for all projects, enabling better analysis, reporting and decision-making. ● SAS Visual Investigator and SAS Intelligent Decisioning were leveraged to enhance application fraud detection ● For the Next Best Action project, by using SAS Model Studio, Machine Learning models were developed to enhance customer engagement and provide personalized offers. ● SAS Model Manager for deployment, monitoring, and updating of models, helping to streamline decision-making processes and optimize real-time decision-making within the bank.   What were the results or outcomes? The implementation of SAS solutions led to: ● Consolidation and standardization of the bank's analytical data platform, improving efficiency and reducing reliance on legacy systems like SPSS and Experian. ● Scalability improvements in key areas such as collections, credit allocation, and fraud prevention, enabling the bank to meet growth targets. ● A more advanced, yet seamless, solution for application fraud detection, enhancing user experience while strengthening security. ● Creation of an analytical CRM environment and the development of a Next Best Action system, supporting aggressive new customer acquisition goals by offering real-time personalized offers to customers. ● Overall, the solutions enabled faster, more informed decision-making, allowing the bank to be more competitive in the market.   Why is this approach innovative? The approach is innovative because it enhances the bank's existing AI capabilities by integrating machine learning models into a unified architecture with SAS Viya, enabling real-time, intelligent decision-making that was not possible with previous legacy systems. The Next Best Action system utilizes machine learning to offer personalized customer experiences, improving engagement and acquisition rates. The bank is planning to migrate existing models from SPSS to SAS Viya, which will further modernize decision-making processes, improve scalability, and eliminate current limitations. Additionally, the hands-on training and knowledge transfer from the local partner SADE empowered the business teams to independently manage and optimize decision flows, ensuring long-term sustainability and faster ROI from SAS solutions. ... View more

Axis Bank Ltd.   Contact: Varnika Mishra   Country: India   Award Category: Innovative Problem Solver   Tell us about the business problem you were trying to solve. Background - RBI in its letter dated January 31, 2024, directed all the banks to streamline the internal compliance monitoring function, by leveraging the use of technology and to integrate all the manual processes into a single system. The regulator felt that there is a need to implement comprehensive, integrated, enterprise-wide and workflow-based solutions/ tools to enhance the effectiveness of this function The Compliance team want to have framework for Management Self-Certification of penalties (MSCS). As first line of defence, business / product or process owner has reviewed all activities performed by unit and confirm that all statutes has been adhere by them. The expectation is to automate the manual process of collation through an appropriate workflow. Challenge - There are some issues that are self-identified by respective departments however same are not recorded on system. Same are circulated through email confirmation to control teams on quarterly intervals basis compliance mail initiation. This activity was supposed to be complete manual and recurring either monthly / quarterly. Business used to coordinate to such departments with actionable to users to report the self identified issues, penalties and statutes in their predefined word template. Frequency of this manual followup was as Self-certification for issues - Quarterly with reporting units Self-certification for statutes - Quarterly with reporting units Self-certification for penalty - Monthly with ~42 departments as is 1. Cpl Ops sends an email on the 25th or 26th of every month to 42 departments of the bank, for seeking the details of the Penalty/ strictures/ show cause notices/warnings/advisories etc. levied / passed by various regulators/ administrative agencies to them, giving them a timeline of 3rd or 4th of the subsequent month. 2. The departments share the details through mail in the excel format along with the supporting documents, the supporting documents can be either in the form of mails, PDF letters or excel files. 3. The data is segregated into penalty, advisories, warnings, SCN etc. regulator wise and collate as received from the departments, in our given format, for further reporting to RBI SSM and other internal departments like Risk, TPP, F&A etc. 4. Every month this data is collated in a single repository for future reference and for SSM reporting or as an when required by any other department and periodic reporting like disclosure and quarterly and annual Tranche reporting What SAS products did you use and how did you use them? Tools used - SAS GCM 9.4, SAS EG, SAS DI, SAS SMC, workflow studio. 1- Created New module as ‘Management Self Identification of Issues (MSII)” to report Self-certified issue by CARO in SAS GCM. Same module for Penalties and Statutes. 2- Created New Calendar for self-certification of issues, statutes & Penalties. Named calendar as “Management Self certification Calendar. Creation and auto trigger of Self certification for issues basis defined in calendar. 3- Developed code such that each unit defined in the self-certification calendar should get auto trigger basis defined frequency for each drop-down value respectively (Issue/Statute/Penalties) 4- Added field validation, warning message , informatory message and access controls to Compliance officers and Spoc. 5- Written code and Added rules for Auto trigger of Self identified issues/penalties/ statutes basis the category selection . 6- Created code for Auto generated emails basis the product / department selection and self certification owner for reminders/ escalation as per the Buisness use case. 7- Found workaround to allow to create multiple issue in single self-certification pertains to his/her assessment unit. 8- Created workflow using workflow studio and given maker checker 9- Developed code to build rules in Visual Analytics and SAS EG for self certification Reports with various filters and break ups in drill down manner 10- Created customized Dashboard for Compliance team in a downloadable format .   What were the results or outcomes? 1- Single Repository of self identified issues , penalties and stautes are also now getting maintained in the system 2- Compliance process for reporting self identified items is 100 % Automated 3- Saved effort of manual email followups of Compliance team with every department from ~1 hour to seconds daily. 4- Response time of data entry from departments of bank is reduced from days to minutes. 5- Module is developed in [predefined template form so User are well versed with the processes and adapted in no time. 6- VA reports developed has made easy reporting to regulators /auditors and senior stake holders. 7- Risk of incorrect data sharing will be eliminated, since self certification Calendar auto trigger inherits the parent issue details. 8- Bank Is RBI complaint 9- Audit trail , Attachments , pdf are now stored and can be referred during audits at any point of time. 10- Maker checker has given a feasibility to users to either reject or approve with comments. 11- . Clear, consistent, and efficient process for obtaining, verifying, and submitting Statutory certifications required on a quarterly basis 12- Report are user friendly and transparent to nth level. 13- Reduced dependencies on Old employees for historical data.   Why is this approach innovative? 1- we have customized SAS code 100 % to accomodate our requirement. 2- Since SAS tools is already procured by Bank and we have SAS skilled resources , we have developed this module from scratch, Inhouse. 3- Challenge was limited to Single repository and Automation. But we have not only automated the data entry , but also given add on features like maker checker , audit trails, attachment , email triggers , reminders, alerts , Calendar selection based Autotriggers etc 4- This Challenge was limited to Compliance team only , but we have developed the module in a way that same effort will work for other control functions like Operational risk , IFC fRR risk also. So , as a IT Support team , we have made our lives easy. 5- This whole workaround has been appreciated by Business since this has a- reduced response time from 24 Hours to minutes. b- Manual intervention reduced to Zero. c- Emails follow up time and effort reduced to Zero d- Cost for development - Zero ... View more

Indostar Capital Finance   Contact: Nitesh Kotian   Country:  India   Award Category: Innovative Problem Solver   Tell us about the business problem you were trying to solve. Analyzing company profit and loss for their respective geography like Zone/region/area/branch. Challenges in Financial Data Analysis 1. Manual Data Mapping: Using Excel to map income and expenses at an organizational level was cumbersome, time-consuming, and prone to errors. 2. Limited Accessibility and Understanding: Not everyone had access to the data, and even fewer had the expertise to interpret it, leading to knowledge silos. 3. Bridging the Business-Finance Gap: Communicating financial insights to non-financial stakeholders was a significant challenge, hindering strategic decision-making. 4. Drilling Down to Base-Level Data: Tracking costs and income required digging deep into the data, which was difficult and tedious using traditional methods. What SAS products did you use and how did you use them? SAS Products: SAS Visualization Analytics and SAS studio (SAS Yiya) Key Actions to Enhance Insights and Decision-Making 1. Data Ingestion and Processing: Leverage SAS Viya's robust data management tools to ingest and process data. 2. Advanced Calculations: Calculate profitability metrics, such as profit margins and cost structures, using advanced calculations. 3. Data Visualization: Create informative visualizations to highlight trends, outliers, and opportunities, facilitating easy communication of results. These key actions enable organizations to unlock valuable insights, drive informed decision-making, and optimize their operations for improved profitability.   What were the results or outcomes? Profitability Dashboard: Empowering Senior Management The Profitability Dashboard was successfully created and deployed, enabling senior management to: - Easily interpret various types of expenses and incomes - Analyze data across different geographies - Make informed, data-driven decisions This intuitive dashboard transformed complex financial data into actionable insights, driving business growth and optimization.   Why is this approach innovative? Innovative Aspects: 1. Advanced Analytics: Leveraging SAS Viya's advanced analytics capabilities to calculate drawdown, max drawdown, and recovery periods provides a more nuanced understanding of portfolio performance. 2. Automated Calculations: Automating these complex calculations saves time, reduces manual errors, and enables faster decision-making. 3. Data-Driven Insights: By applying advanced analytics to portfolio data, investors gain actionable insights to inform investment strategies and optimize portfolio performance. 4. Risk Management: Accurately calculating drawdown and max drawdown enables management to better manage risk, set realistic expectations, and develop strategies to mitigate potential losses. 5. Scalability: This approach can be applied to various asset classes, portfolios, and investment strategies, making it a versatile solution ... View more

Nationwide   Contact: Tim Pickering   Country:  UK   Award Category: Innovative Problem Solver   Tell us about the business problem you were trying to solve. he Risk Decision & Data Science (RDDS) team at Nationwide Building Society collaborated with SAS in 2018 to build an industry first Machine Learning Score for Personal Loans (leveraging SAS Enterprise Miner). This resulted in a 10% reduction in Credit Losses with the same volume of loans booked, and more responsible lending. The business problem was now, how do we continue to drive improvements in Scoring to build on this breakthrough. This is when, through dialogue between Iain Brown (Head of Data Science at SAS) and the Head of the RDDS team at Nationwide, it was agreed we would look to push the envelope further and explore synthetic data via Generative Adversarial Networks (GAN’s). The idea was that through enriching the data with more bad cases via the use of GANs that more accurate predictive models could be trained, leading to improved lending decisions. What SAS products did you use and how did you use them? To unlock this problem, the RDDS team and SAS leveraged the power of the SAS Viya platform, making use of its advanced GAN capabilities and GPU acceleration. This enabled multiple combinations of real and synthetic data to be created and tested to find the best possible uplift for our predictive models.   What were the results or outcomes? Through applying GAN’s to enrich the data it was found that there would be a c.10% uplift in model discrimination, which from past experience would likely translate into c.£100k’s over 5 years in business benefits. RDDS are now looking to mature and develop this PoC further as techniques such as GANs are a trailblazer for how Gen AI and Predictive AI can work together, potentially representing the future of Credit decision modelling.   Why is this approach innovative? This approach is innovative because we are not aware of anyone else in the industry taking this approach, making it a first in this area. This project has been presented at internal and external conferences such as the Gen AI Banking Connect event run by SAS which were received with great interest. This project demonstrates how innovative techniques available through SAS Viya can push the envelope of model performance by combining Generative and Predictive AI. ... View more

Dubai Police   Contact: Saif Al Awar   Country:  UAE   Award Category: Community Uplift   Tell how you've used SAS to have a positive impact on your community. Dubai Police leveraged SAS Analytics to enhance public safety, improve crime prevention, and optimize resource allocation. Here’s how they made a positive impact on the Dubai community: Predictive Policing: Using SAS Analytics, Dubai Police could see dashboards with information from across all crime systems and data and take the right decisions to prevent criminal activities, reducing response times and improving safety. Real-Time Data Analysis: SAS enabled the force to process vast amounts of data from different sources in real-time, allowing for faster and more informed decision-making. Smart City Integration: By integrating SAS analytics with Dubai’s Smart City initiative, police could efficiently manage emergency response, crowd control, and overall public security. Through these efforts, Dubai Police significantly enhanced crime prevention, response efficiency, and public safety, reinforcing Dubai’s reputation as one of the safest cities in the world.   What SAS products are you using and how are you using them? Dubai Police has leveraged several SAS solutions to enhance public safety and operational efficiency. Here are the key SAS products they use and how they apply them: 1. SAS Visual Analytics Usage: Provides analytics and forecasting for all Dubai Police departments and sub departments with more than 400 dashboards built for Dubai Police. Any inspection the commander and his assistants do for the departments, they use SAS Visual Analytics to present all the data about the department and sub departments and take the necessary actions and decisions accordingly. Any foreign delegation of Commanders of Police Departments or Ambassadors or other diplomatic convoy, who visit Dubai Police, they showcase their results and numbers on dashboards built on SAS Visual Analytics Impact: Enables law enforcement to make data-driven decisions quickly. World Class showcase of Dubai Police internationally. 2. SAS Event Stream Processing Usage: Helps analyze vast amounts of structured and unstructured data, in real time to take immediate decisions and actions for all departments in Dubai Police. Impact: Improves situational awareness and crime detection and real time decision making. 3. SAS Data Management Usage: Integrates data from multiple sources (police records, IoT sensors, surveillance) into a centralized platform. Impact: Enhances data accuracy and ensures consistency for better decision-making. By integrating these SAS solutions, Dubai Police has enhanced public safety, improved operational efficiency, and reinforced Dubai’s position as a global leader in smart policing.   What was your most surprising discovery about your work? In the race to become leaders in data-driven decision-making, organizations must prioritize not only the accuracy of their data but also the quality of its presentation. Dashboards play a pivotal role in shaping decision-making outcomes, and ensuring their reliability and impact is critical. The surprising discovery of my work was how I saw the Commanders' growing needs for scalable quality control mechanisms while aligning with organizational goals of precision, efficiency, and innovation. It is really surprising how SAS solutions can ease the complexities of dashboard evaluation and foster a culture of data-driven excellence. This synergy ensures my work is driving towards actionable strategies and inspiration to maintain and excel Dubai Police's data maturity. ... View more

Eastern Caribbean Central Bank   Contact: Leah Sahely and the SES Team of Eastern Caribbean Central Bank (ECCB)   Country:  St. Kitts   Award Category: Innovative Problem Solver   Tell us about the business problem you were trying to solve. The ECCB needed to replace its outdated, manual processes for collecting and validating economic and financial data from its eight (8) member countries. Data submission relied on insecure methods like email and fax, lacked real-time validation, and required significant manual intervention to resolve data quality issues. These inefficiencies led to delays and risks in managing sensitive data. With SAS, predefined templates now ensure data are uploaded in a uniform format, significantly improving consistency across all member countries and reducing errors. What SAS products did you use and how did you use them? • SAS Viya: To manage the centralized data warehouse, perform advanced analytics, and design user-friendly reports and dashboards, which are accessible to external data providers as well as staff within the ECCB, including bank examiners, researchers, and policy advisors. For secure data storage, advanced reporting, and analytics tailored to ECCB’s needs. • SAS Studio: To create and run data queries, transformations, and systematic archiving of uploading CSV files. To streamline data queries, perform data transformations, and enable customizable parameters for data processing. To streamline data queries, perform data transformations, and enable customizable parameters for data processing. • SAS Excel Add-In: To retrieve data, generate reports, and streamline internal processes like the Economic and Financial Review and the Macroeconomic Framework, allowing economists to prepare their reports and conduct analyses more efficiently. It also integrates with tools like Power Query for further analysis, including data cleansing. To simplify user access to data, and provide flexibility in reporting and further data cleaning. • R Integration: Direct interfacing with SAS data for advanced data analytics. • API Integration: To automate the publication of data on ECCB’s website, ensuring real-time updates and greater transparency.   What were the results or outcomes? • Automated data collection and validation reduced manual intervention by 70% and improved data accuracy by 50% • Role-based access controls ensured secure submission and retrieval of sensitive data, with Personally Identifiable Information (PII) restricted to authorized personnel. • Data processing for multiple frequencies (daily, monthly, quarterly, annually) made reporting more flexible and detailed. • Tailored dashboards and reports were created, enhancing insights for decision-making • The API enabled seamless integration with the ECCB’s website, streamlining public access to data.   Why is this approach innovative? In the legacy system, once data were entered and processed, they became the live data available to all internal users. With SAS, the separation of validation and production data sources introduced a critical improvement. This approach ensured that only verified data became available to internal users and the public, greatly improving accuracy and trustworthiness. Further, ECCB eliminated outdated manual processes by introducing an integrated, automated solution. Real-time validation during data submission drastically reduced errors and provided immediate feedback to providers. The ability to use predefined templates ensured uniformity in data submission across all entities. Additionally, the use of SAS Viya’s analytics capabilities allowed ECCB to build comprehensive dashboards and reports that are accessible and customizable, enabling stakeholders to make informed decisions based on accurate, timely data. The integration of APIs for publishing data further streamlined public access, making the data-sharing process seamless and transparent. This modernization transformed ECCB’s operations, setting a benchmark for similar institutions in the region. ... View more

UnitedHealthGroup   Contact: Kishor Devkota   Country:  USA   Award Category: Community Uplift   Tell how you've used SAS to have a positive impact on your community. I built predictive model using Medicare members past sample survey results about their experience with their health benefit plans. Based on the varieties of features related to the member each member is predicted if they experiencing any issue with their plan. The high risk members members were then outreached by the customer service team to check on the members and providing assistance over the phone if they need any help. For example, scheduling appointments, checking medication refills, finding specialty providers, resources for community assistance, rewards available.   What SAS products are you using and how are you using them? SAS Enterprise Guide. Use Predictive modeling Procedures like Proc Reg, Proc Logistics, Proc GLM, Proc Corr etc Data evaluation procedures like proc sql, proc freq, proc univariate, proc gchart etc   What was your most surprising discovery about your work? Impact on connecting with members with need, how thankful they are that there were able to get help over the phone. At the same time, it is a great opportuning to learn what issues the members have which were already not known. ... View more