24th Edition
Model Risk
Navigate the new frontiers of model risk management to incorporate new technology and appropriately validate and govern all model types
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What can model risk and governance professionals do to ensure their approaches can keep pace with the ever-growing complexity of model types and technologies?
Engagement and continuous learning. I coach the importance of our team to be engaged in the industry and their peers. Often, the best advice comes from those who have done it or are in the process of doing it. So participating in conferences (listening but also take the opportunity to ask questions), networking, and looking for opportunities to learn about different modeling types and technologies.
What areas are you looking at over the next 3-6 months in terms of investment for support with your model risk strategies?
Business Model- Identifying the appropriate business model which will maximize the effectiveness of the model risk management program and enables the program to provide meaningful effective challenge across a variety of models in alignment with the organizations risk tolerance and considerate of heightened regulatory scrutiny. Thereafter, strategically implementing policies, processes and people who can effectively implement this vision going forward.
Technology- Carefully evaluating and selecting the appropriate tools and technologies needed to effectively operationalize the model risk management program. That would include those tools leveraged by the model risk management governance team to supporting inventory management, monitoring and reporting, as well as the model validation team to support effective and efficient execution of model validation activities including leveraging automation to minimize dependency on manual process and meet business demand.
What are the biggest priorities within model risk management at the moment?
Staffing- number of staff and competencies to meet increasing scope (AI/ML, qualitative models, climate risk models), emerging technologies such as Generative AI, LLM, and heightened regulatory scrutiny. In addition, promoting and supporting learning opportunities to build up core business competencies and subject matter expertise to address the expanded scope of MRM and democratization of models across the organization.
Efficiency and Effectiveness- Reviewing processes and procedures to drive more prescriptive risk-based processes and leveraging automation where we can to reduce reliance on manual processes and accelerate delivery. This would include a revaluation of the onboarding processes, risk tiering process, control processes, validation processes, and reporting mechanisms (internal and external) to ensure our processes are aligned with the size and complexity of the firm and our resources are deployed to areas/models which pose the most risk of use to the organization.
Technology Upgrades- Evaluating opportunities to redesign, upgrade or change systems which are being used to support model risk management related activities within model governance and model validation.
How can the balance between innovation and risk management be assessed when integrating advanced technologies into models?
Continuous focus on the use case. In my experience, the use of the model and the risk it presents when using the output should play a considerable role in driving adoption and developing the appropriate control environment to support use. Using a pragmatic approach in which innovation is geared towards lower risk use cases (operational and efficiency) while building up stronger guardrails for higher risk uses cases promotes responsible innovation.
As a risk oversight function, we should take a cautious approach but should not be seen or conduct ourselves as a hinderance to innovation. Rather we should develop and refine our processes to be more of a facilitator of responsible innovation. So that means making the right investment in the people, processes, and tools we have at our disposal to promote responsible innovation and keep up with speed to market demands.
Ahead of the GFMI 24th Edition Model Risk conference we spoke with Arnold Pashi, Model Risk Governance Director at First Citizens Bank. In this role, Arnold’s team is responsible for designing, implementing, and overseeing the bank’s Model Risk Management Framework which includes AI/ML, Critical Business Tools, and Project Management. Prior to this role, Arnold worked for Huntington National Bank and previously spent most of his 17-year professional career as a Commissioned Bank Examiner with both the Comptroller of the Currency (OCC) and the Federal Reserve Bank of Cleveland. Arnold started his career as a safety and soundness examiner and transitioned to Market and Liquidity Risk Specialist. Arnold participated or led reviews related to Safety and Soundness, MRM, DFAST, CCAR, MRR, horizontal/coordinated Liquidity and Interest Rate Risk Reviews and participated in rotation assignments within the OCC’s Balance Sheet Management Policy Division.
June 24-25, 2024
Wyndham Boston Beacon Hill | Boston, MA
An interview with Arnold Pashi, Model Risk Governance Director at First Citizens Bank
Arnold will be participating in two panel discussions during day one (4/24/2024) of the 24th Edition Model Risk conference!
For registration pricing and multiple attendee discounts, please contact:
Ria Kiayia
All Rights Reserved. marcus evans ® 2024
Interested? Do you feel you will benefit?
In terms of our format (our events are informal and intimate peer-led meetings), how do you see this assisting you with overcoming some of the challenges you currently face?
The quality of the participants and the variety of institutions which attend provide a great platform to learn about common challenges, emerging risks and strategies to consider as you look to solve them within your institution. The intimate peer led-meetings facilitate and encourage dialogue and a chance to develop a network which can then be deepened post-conference.
Panel Discussion: Adapt audit practices for fast-evolving AI/ML models
- How can we develop agile audit practices to navigate the AI/ML landscape effectively?
- What different controls need to be in place for auditing AI/ML models compared to traditional models?
- What strategies can be employed to ensure audit practices remain current with AI advancements?
- How can auditors ensure comprehensive coverage while auditing increasingly complex AI-driven models?
- How do audit departments work alongside model developers and model validators?
Panel Discussion: Build solid governance uniting diverse expertise across the MRM function
- What is the significance of interdisciplinary teams, particularly in the context of AI governance?
- What are some effective approaches for building and overseeing governance teams that encompass a range of skills and backgrounds?
- How can challenges around uniting individuals with diverse expertise be identified and addressed?
- What are some case studies that showcase successful instances of interdisciplinary cooperation within AI model governance?
