Model Risk in Banking: Why SAS is the Solution You Need

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In today’s intricate world of banking and finance, models sit at the heart of critical decision-making processes—ranging from credit risk assessments and capital adequacy to fraud detection and trading strategies. Let’s not forget the headache of navigating regulatory requirements. With this growing dependence comes heightened exposure to model risk—the potential for adverse outcomes due to errors in model development, implementation, or use.

Understanding Model Risk

Model risk arises when a model's predictions or outputs deviate from actual outcomes, leading to potential financial losses or regulatory repercussions. This risk is amplified as institutions adopt advanced technologies like Artificial Intelligence (AI) and Machine Learning (ML), which, while powerful, can introduce biases or unforeseen errors if not meticulously managed.

The Challenges Faced by Financial Institutions

Financial institutions often grapple with several challenges in model risk management (MRM):

• Complexity of Modern Models: Advanced AI and ML models, though accurate, can be opaque and challenging to validate.
• Fragmented Governance: Disparate systems and siloed departments can lead to inconsistent model oversight.
• Regulatory Compliance: Adhering to evolving regulations like IFRS 9, CECL, and Basel III Endgame requires robust model validation processes.
• Resource Constraints: Limited resources can hinder the thorough documentation and monitoring of models.

Effective MRM is essential to maintain shareholder confidence and comply with regulations set by entities like the European Banking Authority, the US Federal Reserve, and the UK's Prudential Regulation Authority. Enter SAS Model Risk Management — a centralized, robust solution built to ensure that banks and financial institutions tackle model risk with transparency, consistency, and compliance across the entire model lifecycle.

SAS’s Approach to MRM

SAS MRM offers a holistic approach to managing the model lifecycle.

• Model Validation: Supports independent review and validation of models to meet supervisory guidance and business requirements.
• Model Organization: Facilitates the creation of model candidate assessments and comprehensive model inventories.

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• Policy and Documentation: Establishes protocols for model-related issue tracking and documentation management.

• Information Sharing: Enables the construction and dissemination of reports for effective top-down model risk reporting.

This integrated approach ensures that all model risk categories are identified, monitored, and controlled, providing clear oversight throughout the model risk lifecycle.

What Makes SAS MRM Different

SAS distinguishes itself by offering:

• End-to-End Governance: Comprehensive management of the model risk lifecycle, from risk identification to assessment.
• Automated Performance Monitoring: Automated monitoring with threshold alerts to assess model performance effectively.
• Governance for AI/ML Models: Intelligent automation to streamline documentation and governance for AI and machine learning models.
• Systematic Usage Tracking: Capture of model usage data across environments to ensure transparency and compliance.
• Repeatable Processes: Establishment of reliable and auditable processes for model review and validation.
• Centralized Information Management: A centralized model inventory supporting internal policies and procedures across all business units.

These features collectively enhance the ability to manage model risk effectively, ensuring compliance and fostering trust with stakeholders.

To Learn More

To explore how SAS Model Risk Management can transform your organization's approach to model risk, visit Model Risk Management (MRM) Software | SAS. The SAS MRM e-learning course is coming soon.

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