As the banking sector grows increasingly reliant on models for functions like credit scoring, fraud detection, and risk management, the potential for errors and losses escalates. While models are essential for sound decision-making, they also introduce various risks, especially when they are poorly designed or misused. Model Risk Management (MRM) has become a cornerstone for addressing regulatory requirements, reducing operational risks, and enhancing decision-making.

As we move toward 2025, banks must adapt their MRM frameworks to the changing landscape. This blog highlights the five key priorities that should guide every banking executive in their MRM strategies in the coming year.

1. Strengthening MRM Frameworks with AI and Machine Learning

AI and machine learning (ML) are rapidly transforming the banking industry. These technologies help automate processes, improve customer experiences, and extract valuable insights from vast amounts of data. However, they also bring new challenges to model risk management. The complexities of AI and ML models demand more advanced MRM frameworks to address issues like:

• Bias and fairness in AI decision-making
• Explainability of complex models
• Model robustness in diverse conditions

To handle these challenges, banks need to:

• Update MRM policies: Revise existing frameworks to ensure they address the risks specific to AI and ML models, including validation and performance monitoring.
• Invest in expertise: Banks should either build in-house AI/ML capabilities or partner with external vendors to gain the necessary skills for managing AI-driven risks.
• Manage ethical risks: Implement safeguards to minimize biases and promote fairness in automated decisions, ensuring transparency in the model’s outputs.

2. Improving Data Quality and Governance

Data lies at the heart of MRM. High-quality, well-governed data is critical for developing reliable models. As banks face increasing regulatory scrutiny around data usage, they must ensure that their data management practices are robust and transparent.

Key steps for improving data governance include:

• Establishing a comprehensive data governance framework that focuses on data quality, security, and access controls.
• Investing in data quality tools to standardize, clean, and validate the data used in models, ensuring accuracy and integrity.
• Ensuring data traceability: Banks should maintain a clear record of data lineage, documenting sources and transformations to ensure data can be traced back to its origins.
• Complying with privacy regulations: Measures to protect customer data, such as compliance with GDPR or CCPA, are essential for maintaining trust and avoiding legal penalties.

Additionally, as Environmental, Social, and Governance (ESG) models become more prevalent, banks must prioritize the accuracy and reliability of ESG data to ensure informed decision-making.

3. Enhancing Model Validation and Monitoring

Effective model validation and monitoring are essential for mitigating risk throughout the model lifecycle. Rigorous validation ensures models meet their objectives and continue to perform well, while continuous monitoring detects issues before they escalate.

To improve these processes, banks should:

• Implement independent validation: Establish an independent function to assess model performance and limitations.
• Conduct regular reviews: Schedule frequent backtesting, sensitivity analysis, and stress testing to evaluate models under varying conditions and ensure their resilience.
• Leverage monitoring tools: Use technology to automate the tracking of model performance, enabling quick detection of any issues and faster response times.
• Focus on documentation: Keep detailed records of model development, validation, and monitoring processes for transparency and regulatory compliance.

Additionally, banks should explore the potential of Robotic Process Automation (RPA) to improve the efficiency of validation and monitoring tasks, reducing human error and streamlining workflows.

4. Addressing Talent Gaps in Model Risk Management

The growing complexity of models, particularly those powered by AI and ML, necessitates specialized skills in model risk management. However, many banks face challenges in attracting and retaining professionals with the necessary expertise.

To address talent shortages, banks should:

• Identify skill gaps: Conduct assessments to determine where knowledge gaps exist within their current teams and invest in targeted training programs.
• Develop training programs: Offer continuous learning opportunities in areas such as model validation, AI/ML, and data governance to upskill existing staff.
• Attract and retain top talent: Offer competitive compensation, professional development opportunities, and career advancement programs to attract skilled MRM professionals.
• Foster a culture of learning: Encourage collaboration and knowledge sharing to promote innovation and best practices within MRM teams.

By investing in talent development, banks can better navigate the increasing complexity of model risk management and improve their decision-making processes.

5. Enhancing Communication and Collaboration Across Teams

Effective communication and collaboration are vital for managing model risk successfully. Clear communication ensures that everyone, from model developers to senior executives, understands the risks involved and the strategies in place to mitigate them.

Banks should focus on:

• Defining roles and responsibilities: Establish clear responsibilities for model development, validation, monitoring, and reporting.
• Communicating risk appetite: Ensure alignment between the bank’s business objectives and its model risk management approach by clearly defining the level of risk the bank is willing to accept.
• Implementing regular reporting: Provide frequent updates on model performance, risks, and mitigation strategies to senior management and the board.
• Engaging with regulators: Maintain open communication with regulatory bodies to address concerns and stay informed about regulatory changes related to model risk.

Regular and transparent communication within and outside the organization is crucial for maintaining trust and ensuring that MRM processes align with the bank’s overall strategy.

Conclusion

As the banking industry faces increasing pressure from new technologies, evolving regulations, and complex financial models, model risk management has never been more important. By focusing on these five key priorities—AI and ML integration, data quality and governance, model validation, talent development, and communication—banks can strengthen their MRM frameworks, improve decision-making, and maintain a competitive edge in the rapidly evolving financial landscape of 2025 and beyond.