Understanding and Implementing CECL
Sessions include quantification methodology, model risk management for CECL models, and the impacts of COVID-19 on CECL.
Higher credit loss provisions reflected the deterioration of borrower creditworthiness due to the effects COVID-19 is having on the global economy, and CECL has only added additional volatility into this process. It is essential that banks need to account for these likely losses by taking provisions now.
This four-day online course has been designed to specifically meet the needs of those working to implement the current expected credit loss (CECL) regulation. Industry experts will be covering need-to-know topics such as quantification methodology and the impacts of COVID-19.
Attendees with have the opportunity to lean from like-minded practitioners from various organisations and apply best practice approaches to CECL.
What will you learn?
- Key challenges and opportunities of CECL
- How to account for different scenarios
- Quantification methodology such as modelling considerations
- Evaluate CECL assumptions for model risk management
- Model implementation processes and governance
- How to incorporate COVID-19 scenarios
Who should attend?
Relevant departments may include but are not limited to:
- Risk management
- Model validation
- Internal audit
- Quantitative analysis
Our live, virtual training courses have been designed to engage and inspire you. Much more than a webinar, our approach includes:
- Technical content compressed into 60-minute interactive sessions and spread out over two, three or four days
- Facilitated collaboration including Q&A, interactive polling and group workshops
- Live interaction with subject matter experts – get your questions answered in real time
- Receive comprehensive course materials and supporting content from Risk.net to reinforce your learning
- Stay connected with other learners and extend your network by joining our dedicated LinkedIn group for course participants
President & founder
Grigoris Karakoulas is the president and founder of InfoAgora Inc. that has provided risk management consulting, prescriptive analytics, RegTech solutions (CECL/ IFRS9/IRRBB/Basel III) and model risk management services to Fortune-500 financial institutions with multi-million dollar benefits. He is also Adjunct Professor in the Department of Computer Science at the University of Toronto. Grigoris has published more than 40 papers in journals and conference proceedings in the areas of machine learning, risk management and predictive modelling in banking. He is on the PRMIA subject matter boards for Stress Testing and Enterprise Risk Management. He holds a PhD in Computer Science (Artificial Intelligence).