Expected credit loss assessment and monitoring
Allows centralized management and controlled orchestration of the entire expected loss modeling process via a user-friendly dashboard.
A centralized, flexible, high-performance analytics environment.
Flexible reporting
Easily manage accounting rules. Automate disclosures and posting into accounting ledgers.
Open platform
Supports deployment of models built in SAS, Python, R or other programming languages.
Adaptability and scalability
Provides a single management platform and modular structure for customizing workflows to capture data, execute models, and consolidate and report results. Scales to support other risk initiatives, such as enterprise stress testing.
Simplified coding requirements
Enables efficient implementation, increases transparency and reduces maintenance effort.
Full transparency
Supports results with comprehensive audit trails.
Centralized model library
Supports strong model governance and promotes efficient reuse, which reduces model risk.
High-performance capabilities
Provides extremely fast computational speeds through parallel task execution, in-memory processing and grid optimization.
Meet compliance challenges while advancing your risk management and financial reporting capabilities.
Get results quickly.
Take advantage of prebuilt model templates – or use your existing impairment models, regardless of platform – for a faster implementation. A user-friendly dashboard and out-of-the-box visualizations make it simple to capture data, execute models and generate reports. You can easily customize accounting rules and data flows, and quickly produce results to assess the financial impacts of the new impairment standards.
Establish control and transparency.
A model implementation platform with a centralized model library enables you to effectively monitor the entire modeling process – so you can identify, report and resolve issues quickly. SAS Expected Credit Loss delivers a well-controlled, flexible and scalable solution that supports risk and finance integration capabilities, allowing you to address both current and future IFRS 9 and CECL requirements. Automated reconciliations and comprehensive audit trails ensure reliable financial reporting and transparency to meet the stringent expectations of directors, regulators and auditors.
Create sustainability in expected credit loss modeling.
Perform calculations faster than ever before with high-performance analytics. You can streamline processes and eliminate bottlenecks to efficiently meet financial reporting deadlines quarter after quarter. SAS Expected Credit Loss ensures reliability and repeatability, while reducing your code base, for greater transparency and lower ongoing maintenance costs..
Adapt and grow.
A centralized library enables fast, efficient model management, while fostering collaboration between your risk and finance organizations. With the solution's highly flexible, open and user-friendly platform, you can easily assess the impacts of changing data, models and assumptions, as well as add functionality and optimize your workflows over time as needs change..
Explore More on SAS® Expected Credit Loss & Beyond
SOLUTION BRIEF
Discover how to achieve CECL compliance with a controlled analytics platform.
Related Products
Check out some of the leading-edge content packages available for SAS® Expected Credit Loss, all based on the powerful SAS® Platform.
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SAS® Regulatory Content for CECLQuickly meet new US Financial Accounting Standards Board current expected credit loss (CECL) standards with best practices for modeling, workflow and reporting.
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SAS® Regulatory Content for IFRS 17Simplify your transition to the new IFRS accounting standard with best-in-class models, workflow and reporting.
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SAS® Regulatory Content for IFRS 9Accelerate time to benefit and quickly meet new IFRS 9 standards.
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SAS® Risk and Finance WorkbenchOrchestrate all aspects of financial stress test and credit loss reserving processes, and consolidate results from various systems, via a central hub.
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SAS® Solution Content for Stress TestingMeet the challenges of supervisory stress testing, and establish a powerful data and modeling environment for scenario-based planning.
