The financial crisis prompted a deep reflection on the practices of banks and other financial institutions. One of the most significant factors that contributed to poor business decisions—and the subsequent economic fallout—was the use of uninformed analytics. This catalyzed discussions around model risk within financial institutions and among regulators. Today, model risk is in the spotlight more than ever due to new regulations and standards such as IFRS 9 and BCBS 239, which mandate not only the deployment of more models but also enforce stringent model governance. It is now evident that model governance and model risk management (MRM) are essential to avoid past pitfalls and drive successful outcomes in the future.
Defining Model Risk and Models
Model risk is the risk of model failure due to incorrect inputs, flawed assumptions, improper model design, or misuse. Regulatory definitions (from bodies such as the Federal Reserve and OCC) describe a model as “a quantitative method, system, or approach that applies statistical, economic, financial, or mathematical theories, techniques, and assumptions to process input data into quantitative estimates.”
A model consists of three key components:
- Inputs: Data, hypotheses, or assumptions provided by subject matter experts.
- Method: Techniques or algorithms (statistical, mathematical, or judgmental) that transform inputs into outputs.
- Reporting: Systems that convert outputs into actionable insights for business decisions.
Notably, regulators also include quantitative approaches with qualitative inputs, provided the output is quantitative.
The Significance of Model Risk in Banking
Model risk is particularly critical in banking due to the industry’s reliance on predictive analytics for decision-making across various functions, including credit lifecycle management, capital stress testing, fraud detection, and marketing. Poorly designed or misused models can result in financial losses, liquidity shortages, regulatory penalties, and reputational damage.
Regulatory Focus on Model Risk
In response to past failures, regulators have intensified their focus on MRM. The U.S. Federal Reserve’s ‘Supervisory Guidance on Model Risk Management’ and the EU’s ‘Supervisory Review and Evaluation Process’ underscore the importance of structured MRM frameworks tailored to the materiality of models and institutions.
Establishing an Effective Model Risk Management Framework
Effective MRM necessitates controlling the entire model lifecycle—from development and data management to deployment—within a robust governance framework approved by the Board and overseen by senior management. Key elements include:
- People: Adequate organizational structure and skilled human resources.
- Processes: Robust MRM processes ensuring prudent model development, validation, and usage.
- Systems: Advanced MRM technology for efficient model management.
Empowered’s Role in Enhancing MRM
Empowered provides technology solutions that enable financial institutions to:
- Develop and manage more models in response to regulatory requirements like Basel III and IFRS 9.
- Enhance model risk management practices with increased frequency and accuracy.
- Improve organizational model risk awareness through comprehensive reporting and analysis tools.
- Ensure regulatory compliance across the entire model lifecycle.
With Empowered’s advanced technology, banks can establish a resilient MRM framework that not only meets regulatory demands but also enhances operational efficiency and decision-making accuracy. By balancing human resources, processes, and technology, Empowered empowers financial institutions to mitigate model risk effectively and foster sustainable growth.
