4th Edition Model Risk
Management
Master efficiency, AI implementation, and current model risk management best practices by optimising model frameworks, risk quantification, and resources in line with industry and regulatory shifts
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Are machine learning or statistical modelling skills more valuable in today's MRM landscape?
Statistical modelling skills are more prevalent at present but the potential value add from machine learning means we should be looking to integrate these capabilities at the earliest opportunity.
What areas of model risk management should banks be investing in right now? What about in 3-6 months?
A good model inventory is at the crux of model risk management; as such, investment in internal digitalisation is a priority to better manage the challenges of a rapidly evolving model landscape. Other priorities include the development of cyber risk models to offset the increased threat of cyber attacks, the development of in-house expertise on developing and validating climate change models, and working on operational resilience as a response to skills shortages in the model risk management space.
How has model risk management evolved in the last few years and what new skills are crucial for successful professionals?
Model risk is now a material risk for many banks reflecting a more holistic view of their model risk and the greater degree to which their model risk management frameworks have been embedded. Management has evolved from a reactive to a proactive approach, i.e. going from doing the minimum to converging towards best practice. All areas of the model lifecycle are in focus.
New skills include knowledge of data science; the ability to develop AI and machine learning models as well as digital skills and technical versatility. All of these should be supplemented by competencies on the business and regulatory side.
Given the rise of fintechs and digital banks, is there new competition in hiring for MRM?
Absolutely. It’s a jobseeker’s market at present and candidates are drawn to the innovative and fast-paced environment of fintechs as well as to the level of compensation offered. The intense competition not only makes it increasingly difficult to recruit critical talent but also contributes to retention risk.
Ahead of the 4th Edition Model Risk Management conference we spoke with Angela Flynn, Head of Group Model Risk at AIB. Angela is Head of Group Model Risk at Allied Irish Bank. She joined the bank from the Retail Risk Analytics team in HSBC Hong Kong where she spent 7 years working on Basel models, governance, and IFRS9. Prior to that she worked in the Group Model Validation team at Standard Chartered in Singapore and in Portfolio Management and ICAAP for Lloyds Banking Group in the UK. Angela has a PhD in Astronomy.
13-15 June, 2022
Paris, France
Angela will be presenting a session and a panel discussion during the 4th Edition Model Risk Management conference!
Session: Evaluate the skills needed for the future professional profile in model risk management
- Examine the new and emerging needs in the field
- Discuss the competition for talent in programming, finance, and data science
- Develop a well-rounded professional profile for model risk which reflects the adoption of holistic frameworks
For registration pricing and multiple attendee discounts, please contact:
Ria Kiayia
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Interested? Do you feel you will benefit?
An interview with Angela Flynn, Head of Group Model Risk at AIB
Panel Discussion: Examine the application benefits of model inventories versus the benefits of a comprehensive model risk management system
- What is needed when creating a more holistically-structured model risk framework?
- How can internal and regulatory needs be aligned?
- Which model types benefit from a comprehensive risk management system?
- What direction is regulation heading and how can banks efficiently adapt?
What do you hope to gain from the Model Risk Management meeting, and how will your session inform the attendees?
I’m excited to learn more about the application of AI models and how they have been integrated into Model Risk Frameworks. I’m also curious to hear others’ thoughts on what types of new models should be on our radar.
In my session I hope to prompt some discussion as to what comprises the optimal skill set in the model risk space.
