Solution Brief
Expected Credit Loss Process Optimization
Satisfy the expected loss accounting standards with confidence and efficiency.
The issue
With economic uncertainty and a slew of regulations, financial institutions face enormous challenges today. While banks have responded by strengthening their balance sheets, many continue to strain under increasing regulatory and business demands.
Consider the U.S. accounting standard for credit loss reserving Current Expected Credit Loss (CECL) or the international accounting standard IFRS 9. They greatly increase the complexity of the allowance estimation process, as it requires much more data than past practices. In addition, data is typically fragmented and of varying availability and quality.
Compliance is no small task, partly because the standards are recent and implementation specifics continue to evolve. The process requires integrating risk and finance; building, testing and managing new models; and managing complex data and model risk.
What’s needed is a robust, transparent and sustainable expected credit loss process that can be implemented quickly and easily adapted to changing interpretations over time.
The challenge
Managing regulatory burdens
Modeling assumptions and limitations must be well understood and documented – a challenging task as banks create large numbers of models. SAS automates workflows, simplifies working with large data sets, and speeds impairment calculations for each production cycle.
Preparing for business impacts
Because CECL and IFRS 9 can significantly raise loss reserve requirements and introduce new volatility, banks must proactively manage their balance sheets. SAS delivers a common platform to quantify the effects of changing scenarios and assumptions.
Interpreting and explaining results
Stakeholders now demand clear and concise information to support reserve estimates. SAS provides full transparency into calculations and powerful visualizations to summarize and explain the results.
Maintaining efficiency
In an uncertain environment, banks need fast data to support strategic decisions. SAS scales to provide rapid analysis of even the largest data sets.
Responding to change
Banks need a solution that can adapt to changing CECL and IFRS 9 interpretations and business requirements. SAS makes it easy to add new functionality and refine processes.
Our approach
What’s needed is a CECL or IFRS 9-compliant process running on a data and AI platform that supports other risk processes. We approach this problem by providing software and services to help you:
Centrally orchestrate workflows
Capture data, execute models, and consolidate and report results in a well-controlled environment.
Create models and run scenario- and simulation-based analyses
Use prebuilt models ranging from roll rate models to Monte Carlo state transition models or create your own using a simplified user interface.
Deploy existing models
Use an open implementation platform to manage and deploy proprietary models, whether coded in SAS or other languages.
Streamline management of complex, granular models
Use a model implementation platform to manage models and optimize their performance. A centralized library supports versioning, promotes sharing of best practices and reduces model risk.
Process large data sets in near-real time
Harness the power of distributed, in-memory processing. Perform on-the-fly aggregations and drill downs of results.
Adapt over time
Add new functionality as needs and regulations change.
One thing that drew us to SAS is that it’s an open solution –we’re able to dive into the data because the solution is not a black box, A phrase we commonly say in the industry is ‘trust but verify,’ and with the CECL solution, we’re able to pull data out, verify the information and validate that the information coming out of the solution is trustworthy, which is key for us.Erich Reuter Executive Vice President of Quantitative Analytics and Enterprise Stress Testing, TowneBank
SAS difference
With SAS, you can deploy a robust, transparent, sustainable CECL or IFRS 9 process that scales with your business, adapts to changing regulatory interpretations and delivers:
A flexible environment
- Adapt to institution-specific workflows and incorporate existing models, regardless of the platform.
Efficient implementations
- Quickly and efficiently implement a robust, sustainable production process.
Improved execution with high-performance data and AI
- Rapidly execute complex, granular models of large data sets and run quick, on-the-fly aggregations and drill downs, shortening implementation time and production cycles.
Freedom to use in-house capabilities
- Incorporate models built in Base SAS, Python or R into an efficient, controlled execution environment.
- Accelerate their performance with distributed processing.
Better coordination and control
- Use a single, integrated, controlled system to manage data and model inventories, execute models and prepare journals for accounting.
Platform flexibility
- Support CECL and IFRS 9 and regulatory stress testing workflows in a single framework to operate more efficiently while reducing implementation and execution risks.
Streamlined model development
- Accelerate model development and enable end-to-end life cycle management for your entire model inventory.