SAS is the ONLY vendor to score HIGH across ALL criteria
SAS offers a complete MRM solution that enables firms to organize a centralized model inventory – complete with an assessment of candidate models – that supports theoretical and assumption documentation, model limitation scoring, validation results, criticality ratings, and model interdependence relationships.
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- Analyst Report SAS is a category leader in Chartis RiskTech Quadrant for Model Risk Management, 2021SAS is a category leader in Chartis RiskTech Quadrant for Model Risk Management, 2021.
- White Paper Compete and win with better model risk managementAs explored in this paper, models can degrade over time, and sound model risk management (MRM) is the key to managing this risk.
- Analyst Report Chartis RiskTech Quadrant: Technology Solutions for Credit Risk 2.0 (Banking Book)This research paper is based on material originally published in the Chartis Research report Technology Solutions for Credit Risk 2.0: Vendor Landscape, 2019.
- White Paper The Future of Model Risk Management for Financial Services FirmsBanks have been using credit scoring models for decades, but since the financial crisis of 2008, regulators have formalized the discipline of model risk management (MRM), driving the need for more rigorous, enterprise-level model information management. Regulators now want to evaluate bank models to access their trustworthiness – not blindly accept the numbers they generate.
- Customer Story A model solutionTD Bank uses SAS Model Risk Management to stay on top of regulatory requirements, facilitate cross-functional collaboration and drive business value.
- White Paper Managing Models and Their RisksComputational and technological challenges present opportunities for a fast-evolving risk management discipline.
- Event Collateral White Paper Model Risk Management: Today's Governance and Future DirectionsA GARP-SAS Survey on Model Risk in the Age of Artificial Intelligence and Machine Learning.
- White Paper Machine Learning Model GovernanceBanks are rapidly expanding their use of machine learning-enabled (ML) models, because they can provide step-level improvements in accuracy. But ML models need even more rigorous governance than traditional models. This paper explores what's required to implement effective ML model governance.