Model risk management: fundamentals
View AgendaKey reasons to attend
- Identify the key drivers of model risk in 2025
- Learn how to design and implement a robust model risk management (MRM) framework
- Gain insights into the applications of new technologies in MRM
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About the course
With the rapid adoption of big data, advanced analytics and AI-driven models, model risk management frameworks must evolve to address an increasingly complex risk landscape. In this course, participants will explore the design and implementation of a robust MRM framework that responsibly incorporates artificial intelligence.
Attendees will examine industry best practices, the regulatory landscape and techniques for enhancing model robustness while adapting to market volatility. Expert practitioners will discuss pricing and credit risk models, using case studies and discussions to reinforce key concepts.
Participants will leave this training with the knowledge to strengthen their firm’s MRM framework, leading to improved risk management and business decision-making.
What participants say:
“Over the course of the three days training, i gained valuable insights and knowledge, even building upon concepts I was already familiar with. The speakers were highly engaging and demonstrated expertise, making the learning experience both enjoyable and impactful. I left the training with a deeper understanding of Model Risk and several actionable takeaways to share with my team and stakeholders at NWG. Overall, I found the course to be incredibly worthwhile and would highly recommend it to others.”
Pricing options:
- Early-bird rate: save up to $800 per person by booking in advance
- 3-for-2 rate: save over $3,000 by booking a group of three attendees
- Subscriber reward: save 30% off the standard rate if you are a Risk.net subscriber
- Season tickets: cost-effective option for groups of 10 or more. Learn more
*T&Cs apply
Learning objectives
- Explore best practices for pricing models
- Discuss the effective management of credit risk models
- Learn techniques to enhance model resilience in times of market volatility
- Examine how to set clear model risk appetite
- Understand regulatory guidance for model risk management
- Explore the role of artificial intelligence (AI) in MRM
- Gain insights into effective model risk governance
Who should attend
Relevant departments may include but are not limited to:
- Risk management
- Model risk
- Model validation
- Compliance
- Regulation
Agenda
December 15–17, 2025
Live online. Timezones: Emea/Americas
September 22–24, 2026
Live online. Timezones: Emea/Americas
Tutors
Grigoris Karakoulas
President
InfoAgora Inc
Grigoris has over 26 years of experience in predictive modelling and risk management. He is the president and founder of InfoAgora that provides risk management consulting and more to financial services organisations. He is an adjunct professor in the department of computer science at the University of Toronto.
Prior to founding InfoAgora, Grigoris was working at CIBC as vice president of customer behavior analytics, responsible for customer decisioning and credit risk measurement solutions for adjudicating new customers and proactively managing existing ones. He has been a postdoctoral fellow in the Institute of Information Technology at the National Research Council. He is on the PRIMA subject matter boards for stress-testing and enterprise risk management and has published more than 40 papers in journals and conference proceedings. He holds a PhD in computer science.
Caterina Dalmara
Lead, Global Model Risk Function
ING
Caterina has worked for almost 20 years in modelling, validation and other model risk functions, including audit and the regulator. She carries deep knowledge of risk modelling techniques and is currently a lead in the global model risk function of ING.
Juan Gonzalez Herrera Risk Learning Faculty
Vice President
State Street
Juan has over 20 years of experience working in risk management and derivatives, including modelling and model risk management. He has expertise in market risk assessment, reporting, and modelling with wide-ranging exposure to banking markets in the US, Canada, LATAM, Europe and China. Prior to his current role as vice president, market risk management, he was vice president of model risk management for almost seven years at State Street.
Gary van Vuuren
North-West University
Gary trained in physics and mathematics at the University of Natal where he obtained a Masters in astrophysics and a PhD in nuclear physics. He worked at the Atomic Energy Corporation but then transferred to finance after visiting the UK and working as a quantitative analyst at Goldman Sachs. He returned to South Africa and worked for ABSA in market risk management and then as an investment analyst at Old Mutual Asset Managers in Cape Town (when he obtained a Masters in market risk management).
In 2002, he migrated to the UK where he worked as in market risk for Standard Bank (during which time he obtained the Global Association of Risk Professionals Financial Risk Manager accreditation), then head of quantitative analytics at Ernst & Young, product control at Merrill Lynch, head of model validation at Fitch Ratings (when he obtained his PhD in cred-it risk) and finally as head of model validation for Aviva Investors.
He has worked as an independent consultant on quantitative and risk management projects for the European Central Bank and quantitative credit risk assessment and management in financial institutions in Antwerp (Belgium) and Utrecht (Netherlands). His current roles include: contracting for EY and RiskWorx (South Africa), a distinguished professor in risk management and head of the University of the Witwatersrand’s Fintech Hub, a visiting professor at the University of Pretoria, the University of Cape Town, North-West University, EDHEC and IESEG (France) and Brunel University, Metropolitan University and Sussex University (UK).
Accreditation
This course is CPD (Continued Professional Development) accredited. One credit is awarded for every hour of learning at the event.
Pre-reading materials
The Risk.net resources below have been selected to enhance your learning experience:
- Model risk frameworks
- An AI-first approach to model risk management
- Reimagining model risk management: new tools and approaches for a new era
A Risk.net subscription will provide you access to these articles. Alternatively, register for free to read two articles.
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