List of SAS Infrastructure for Risk Management Customers
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Since 2010, our global team of researchers has been studying SAS Infrastructure for Risk Management customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased SAS Infrastructure for Risk Management for Database Management from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using SAS Infrastructure for Risk Management for Database Management include: Bank of Montreal, a Canada based Banking and Financial Services organisation with 53597 employees and revenues of $25.19 billion, Banca Mediolanum, a Italy based Banking and Financial Services organisation with 3442 employees and revenues of $1.80 billion, TowneBank, a United States based Banking and Financial Services organisation with 3000 employees and revenues of $692.0 million, Bendigo Bank, a Australia based Banking and Financial Services organisation with 4777 employees and revenues of $324.0 million and many others.
Contact us if you need a completed and verified list of companies using SAS Infrastructure for Risk Management, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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Banca Mediolanum | Banking and Financial Services | 3442 | $1.8B | Italy | SAS Institute | SAS Infrastructure for Risk Management | Database Management | 2021 | n/a |
In 2021, Banca Mediolanum implemented SAS Infrastructure for Risk Management. The deployment used SAS Viya together with SAS Risk Modeling and Model Studio to improve credit scoring and credit-risk model development in Italy, aligning the implementation to the Database Management application category.
Banca Mediolanum configured SAS Viya as the platform layer for model development and model governance, using SAS Risk Modeling for credit score construction and SAS Model Studio for iterative model building and validation. The SAS Infrastructure for Risk Management configuration centralized model artifacts and training data, and leveraged platform services for reproducible model pipelines and structured model metadata typical of Database Management solutions.
Operational coverage focused on the bank s credit risk and analytics functions in Italy, supporting regulatory reporting pipelines and credit scoring workflows. Governance changes emphasized standardized model lifecycle processes, model documentation and validation workflows to increase model reliability and transparency, and the deployment aimed to accelerate speed to market for regulatory reporting.
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Bank of Montreal | Banking and Financial Services | 53597 | $25.2B | Canada | SAS Institute | SAS Infrastructure for Risk Management | Database Management | 2017 | n/a |
In 2017, Bank of Montreal implemented SAS Infrastructure for Risk Management. The deployment centered on Database Management to support IFRS 9 impairment modeling, expected credit loss provisioning and data quality assurance for stage allocation across credit instruments.
SAS Infrastructure for Risk Management was configured with SAS Business Rule Manager as the primary rule orchestration layer, alongside ETL development and components of SAS RFW, SAS IRM and SAS Visual Analytics. Teams used BRM UI functionality to create input and output vocabularies, author and modify core and dynamic rules, publish rule flows and import and update lookup tables. Developers also built base SAS code and reusable macros to replicate UI logic, run scenario analyses, implement a notch framework for significant increase in credit risk comparison and produce impact sensitivity runs.
Operational processes included automated job flows for concurrent scenario runs, quarterly updates to macroeconomic scenario weighting provided by GAC, and procedures for minimizing columns and calculations to compare pre and post results and generate exception reports. Code packages were maintained in Bitbucket and administrators were supported in application deployment with pre and post deployment validation documents, including lists of change requests and incident reports for audit purposes. The solution drove stage allocation decisions used to assign 12 month or lifetime expected credit loss treatments.
Governance activities included participation on the User Acceptance Testing team to identify and resolve defects prior to implementation events, maintenance of agile documentation for transition and ongoing support, and elimination of manual data processes. Performance and enhancement work reduced run time and disk space, and SAS Visual Analytics dashboards were created to present Final ECL impacts by stage, risk segment and model or macroeconomic changes to help stakeholders assess sensitivity. The implementation combined Database Management, rule orchestration, analytics and automated operational workflows to support Bank of Montreal's IFRS 9 provisioning processes.
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Bendigo Bank | Banking and Financial Services | 4777 | $324M | Australia | SAS Institute | SAS Infrastructure for Risk Management | Database Management | 2017 | n/a |
In 2017, Bendigo Bank deployed SAS Infrastructure for Risk Management to centralize its credit-risk and loan-portfolio data. The deployment is framed within Database Management and consolidated approximately 80% of the bank's credit and loan-portfolio datasets onto a single governed platform to support credit modelling and stress testing.
SAS Infrastructure for Risk Management was configured to provide a single environment for credit modelling workflows and stress testing, with emphasis on data consolidation, controlled model development, and governed data access. Functional capabilities implemented included centralized credit-risk data stores, model lifecycle control and a governed analytics environment, consistent with Database Management practices for risk functions.
Operational coverage focused on the bank's credit risk and loan portfolio teams, unifying data previously distributed across multiple repositories and enabling risk analytics to run against a common dataset. The platform moved large volumes of data within months during the rollout, indicating a bulk data consolidation effort to a centralized Database Management platform for risk operations.
Governance changes included establishing a single governed environment for modelling and testing and formalizing data stewardship and model controls for credit risk. The implementation explicitly improved agility and reduced maintenance costs for credit-risk data management, as reported in the customer case study.
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Banking and Financial Services | 3000 | $692M | United States | SAS Institute | SAS Infrastructure for Risk Management | Database Management | 2018 | n/a |
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Buyer Intent: Companies Evaluating SAS Infrastructure for Risk Management
- Merton Council, a United Kingdom based Government organization with 2474 Employees
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