Credit risk model management

  • 3 days
  • Quant & model risk
  • 8 CPD points
View Agenda

Key reasons to attend

  • Learn how to build and maintain a framework to validate credit risk portfolio models

  • Understand the impact of Basel 3.1 amendments

  • Approach the guidelines and implications of artificial intelligence (AI) in credit risk modelling

Find out more

Customised Solutions

Does your team require a tailored learning solution on this or any other topic?

Working with the portfolio of expert tutors and Risk.net’s editorial team, we can develop and deliver a customised learning to make the most impact for your team, from initial assessment to final review. 

Find out more

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-testing on 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.

AI and machine learning application in credit risk modelling session will focus on utilising smaller data, likewise the participants will learn how to process profits in model risk management.

Flexible pricing options:

  1. Early-bird rate: book in advance and save $200 

  2. 3-for-2 group rate: book three delegates for the price of two and save more than $2,000 

  3. Season tickets: book a team of 10 or more and save up to 50%

Learning objectives

  • Evaluate model risk management and governance through different framework
  • 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: 

  • Credit risk

  • Risk modelling 

  • Risk management

  • Model risk management

  • Climate risk

  • Stress testing 

  • Machine learning

Agenda

March 2022, 2023

Time zones: Emea / Americas 

Start: 13:15 GMT / 08:15 EST
Finish: 17:00 GMT / 12:00 EST

Sessions:

  • Model risk management and government for credit risk

  • Credit risk model validation

  • Stress testing credit risk portfolios

  • COVID-19 impact on credit risk modelling

  • Developments for credit risk modelling

  • Credit risk modelling post-IFRS 9

  • Application of AI and machine learning in credit risk modelling 

  • Integrating climate risk and credit risk

  • Model risk management processes for profit

VIEW DETAILED AGENDA


July 17–19, 2023

Time zones: Emea / Americas

REQUEST DETAILED AGENDA

Tutors

Maria Kostova

Lead quantitative specialist

CRISIL Limited

Grigoris Karakoulas Risk Learning Faculty

President

InfoAgora Inc

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:

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

Registration

March 20 - 22, 2023

01:15 pm - 05:00 pm

Virtual

Price

$2,199

Earlybird Price

$1,999

July 17 - 19, 2023

02:00 pm - 05:00 pm

Virtual

Price

$2,199

Earlybird Price

$1,999
Book now

Enquire about:

  • Agenda and registration process
  • Group booking rates
  • Customisation of this programme
  • Season tickets options
Contact us

You need to sign in to use this feature. If you don’t have a Risk.net account, please register for a trial.

Sign in
You are currently on corporate access.

To use this feature you will need an individual account. If you have one already please sign in.

Sign in.

Alternatively you can request an individual account here: