Credit risk model management
View AgendaKey reasons to attend
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Learn how to build and maintain a framework to validate credit risk portfolio models
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Understand the impact of Basel 3.1 amendments
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Approach the guidelines and implications of artificial intelligence (AI) in credit risk modelling
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About the course
Join Risk Learning and faculty members on this interactive learning event examining the current outlook for credit risk model management.
Participants will learn how to validate credit risk models and stress-test credit risk portfolios. Sessions will also explore the developments in credit risk such as the impact of Basel 3.1 amendments, expected economic trends in 2023 and estimation and mitigation of uncertainty.
Sessions include AI and machine learning applications in credit risk modelling, as well as processing profits in model risk management.
Flexible pricing options:
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Early-bird rate: book in advance and save $200
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3-for-2 group rate: book three delegates for the price of two and save more than $2,000
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Season tickets: book a team of 10 or more and save up to 50%
Learning objectives
- Evaluate model risk management and governance through different frameworks
- Conduct impactful general principles of model design in stress-testing
- Address the developments for credit risk modelling by adjusting to new regulations
- Identify credit risk modelling in post-IFRS 9 with Basel 3.1 amendments
- Implement the application of AI and machine learning using smaller data
- Conduct the integration of climate risk on the PD model curve
Who should attend
Employees whose job responsibilities may include but are not limited to:
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Credit risk
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Risk modelling
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Risk management
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Model risk management
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Climate risk
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Stress testing
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Machine learning
Agenda
March 20–22, 2023
Time zones: Emea/Americas
Sessions:
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Model risk management and government for credit risk
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Credit risk model validation
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Stress testing credit risk portfolios
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COVID-19 impact on credit risk modelling
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Developments for credit risk modelling
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Credit risk modelling post-IFRS 9
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Application of AI and machine learning in credit risk modelling
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Integrating climate risk and credit risk
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Model risk management processes for profit
July 17–19, 2023
Time zones: Emea/Americas
Tutors
March 20–22, 2023
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Dr Jonathan Schachter, Financial services consultant via delta vega, Jefferies
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Grigoris Karakoulas, President, InfoAgora
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Navin Rauniar, Partner, Tata Consultancy Services
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Maria Kostova, Credit risk quantitative manager, financial services risk management, EY
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Sebastián Fernandez, Senior manager of credit risk assurance, KPMG UK
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Chris Goodrum, Manager, Credit risk specialist, KPMG UK
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Eleimon Gonis, Risk governance senior manager, KPMG UK
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Thomas Gadd, Assistant manager of climate risk modelling in banking, KPMG 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:
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US Bank cautions on regulators’ TLAC proposal - Read article | Risk.net
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Banks temper credit loss models by editing Covid narrative - Read article | Risk.net
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Modeling credit risk in the presence of central bank and government intervention - Read article | Risk.net
To access some of the above articles you need to have a current subscription to Risk.net. If you don’t have one now, please subscribe to a free trial
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