SAS IS A CATEGORY LEADER
Chartis RiskTech Quadrant for Credit Portfolio Management Solutions, 2023
SAS scored four out of four stars in all six vendor capability measures. According to this Chartis report, our solutions can integrate various components smoothly throughout the credit management process. From data management and credit scoring to risk modeling and compliance, the platform creates a unified environment.
SAS stands out in the CPM landscape for its strong suite of advanced analytics capabilities. In an industry where practical insights are crucial, SAS’ expertise in handling complex statistical models and predictive analytics is particularly advantageous for financial institutions looking for a nuanced approach to credit management. This capability isn’t just theoretical – it is a practical tool for those dealing with the complexities of CPM for which there is value in actionable analysis.
Explore More SAS Resources
To browse resources by type, select an option below.
-
- Select resource type
- Аналитический отчет
- E-Book
- Технический документ
- Технический документ
- Article
- Blog Post
- Book Excerpt
- Case Study
- Infographic
- Interview
- Research
- Series
- Video
- Вебинар
- Customer Story
-
Аналитический отчет
Chartis RiskTech Quadrant Asset and Liability Management, 2024SAS is named a category leader in Chartis Research's 2024 RiskTech Quadrant for ALM solutions, FTP solutions, LRM and reporting technology solutions, capital and balance sheet optimization solutions., hedging and risk management solutions, and financial planning and budgeting solutions.
-
Аналитический отчет
Chartis RiskTech100 2025SAS ranks #2 overall in the prestigious Chartis RiskTech100, 2025. Six category wins are AI for Banking, Balance Sheet Risk Management, Behavioral Modeling, Enterprise Stress Testing, IFRS 9 and Model Risk Management.
-
Аналитический отчет
Chartis RiskTech100 2024SAS climbs to No. 2 in the prestigious Chartis RiskTech 100®, 2024, and bested seven technology award categories, including AI for Banking, Behavioral Modeling and Enterprise Stress Testing.
-
E-Book
Unifying Model Management Across the BankHow banks can empower all departments to manage model risk effectively across the entire model life cycle.
-
Технический документ
Tackle the Complexity of IFRS 9 and CECL StandardsThe US standard for CECL increases the complexity of the allowance estimation process. Outside the US, IFRS 9 is having the same effect. Learn about best practices for getting this right.
-
Технический документ
CECL: Don't Neglect the FundamentalsFirms that proactively implement a CECL process that is controlled, efficient, collaborative and sustainable will find themselves with a competitive advantage over time. This paper discusses the long-term benefits of this holistic approach.
-
Технический документ
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.
-
Технический документ
Basel IV: The push you neededIn a landscape of great uncertainty and the economic crisis sparked by COVID-19, financial institutions must address the challenges Basel IV will bring. An integrated risk management approach is the best path forward to meeting ever-evolving regulatory needs.
-
E-Book
Adapting to the New Age of Risk AnalyticsRapid advancements in technology are leading to a new age of risk analytics. The availability of commercial and open source software – coupled with significantly improved integration using industry standard tools – has made analytics more user friendly, expanding its reach to a broader range of business professionals.
-
Event Collateral
Технический документ
Model Risk Management: Today's Governance and Future DirectionsA GARP-SAS Survey on Model Risk in the Age of Artificial Intelligence and Machine Learning.
-
Технический документ
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.
-
Технический документ
Risk-Aware Finance and the Changing Nature of CreditNew research by Chartis and SAS highlights how financial institutions must align finance and risk departments to accurately assess future risks and bolster budgeting and forecasting capabilities. This paper explores how risk-aware finance is becoming essential to meeting future regulatory and competitive demands.
-
Article
IFRS 9 and CECL: The challenges of loss accounting standardsThe loss accounting standards, CECL and IFRS 9, change how credit losses are recognized and reported by financial institutions. Although there are key differences in the standards for CECL (US) and IFRS 9 (international), both require a more forward-looking approach to credit loss estimation.
-
Article
CECL: Are US banks and credit unions ready?CECL, current expected credit loss, is an accounting standard that requires US banking institutions and credit unions to estimate life-of-loan losses at origination or purchase.
-
Article
МСФО 17: нет времени на раздумьяМСФО 17 - это основанный на принципах стандарт бухгалтерского учета для ориентированной на будущее оценки договоров страхования. Предназначенный для повышения финансовой прозрачности, МСФО 17 требует, чтобы страховщики более подробно сообщали о том, как договоры страхования и перестрахования влияют на их финансы и риск.
-
Article
Credit risk management is the answerLending and loan volume is back up to pre-crisis levels. But banks are facing higher delinquencies as well. That's why improving credit risk management is crucial.
-
Article
Risk data infrastructure: Staying afloat on the regulatory floodWhat are the challenges of a risk data infrastructure and how can they be addressed? Here's what you need to know to build an effective enterprise risk and finance reporting warehouse.