Course Agenda

Day one

8:30	Registration and refreshments
9:00	Model risk management & governance Definitions of models and model risk Model types and model approaches Sources and key drivers of model risk Evolution of model risk management OSFI guidelines and other regulations on model risk management Model governance framework including three lines of defence Coverage of model workflows including model interconnectivity *Speaker:* *Yaping Jiang, managing director, JW Matrix*
10:30	Morning break
11:00	How to build a model risk management framework Development, quantification, integration, implementation Setting risk appetite, policy & standards for model risk Model inventory process Model lifecycle management (development, validation, implementation, use, periodic review) Estimating capacity for risk Speaker: *Olga Streltchenko, director, Export Development Canada*
12:30	Lunch
1:30	Model validation & performance analysis What is validation? Improving the models Validation tools Performance analysis review How to quantify model limitations Vendor and third-party model validation
3:00	Afternoon Break
3:30	Model risk management for pricing models Best approach to pricing models Products in balance sheet Market of products vs. pricing and hedging Source of valuation adjustments in pricing Identification and mitigation of model and input risk Establishing pricing and validation framework
5:00	End of day one

Day two

8:30	Refreshments
9:00	Model risk management of non-pricing models Retail models (credit scoping/marketing) IFRS 9 overview & progress since implementation The similarities and difference of CECL compared to IFRS 9 and other regulatory credit models Sources of model risk in IFRS 9 models What new strategies need to be put in place for testing IFRS 9 models and assumptions? Speaker: Grigoris Karakoulas, president and founder, InfoAgora Inc.
10:30	Morning break
11:00	Utilizing machine learning for model validation and monitoring of valuation models Why banks need larger validation throughput and how to use AI to speedup Measuring data quality with ML Building AI challenger models for model risk uncertainty measurement Generating test scenarios with ML Solving PDE’s with deep reinforcement learning Validation with AI of market data generation algorithms (IR curve building, volatility surface construction) Monitoring valuation models with ML (PnL & XVA)
12:30	Lunch
1:30	New techniques for validation of machine learning models Session points TBC Speakers: Greg Kirczenow, senior director, enterprise model risk management, RBC Ali Fathi, senior manager, AI research and methodology, enterprise model risk management, RBC Vathy Kamulete, senior manager AI research - enterprise model risk management, RBC
3:00	Afternoon Break
3.30	Model risk into the future Applying models to new challenges Data challenges Automation vs. human judgement Big data and advanced analytics Treatment and governance of near-models and non-models Future of regulation; possible futures Further evolution of models *Speaker: Alex Shipilov, president, iGRC Advisors*
5:00	End of course

Course Agenda

Agenda

Model Risk Management - Toronto

Course Agenda

Day one

Registration and refreshments

Model risk management & governance

Morning break

How to build a model risk management framework

Lunch

Model validation & performance analysis

Afternoon Break

Model risk management for pricing models

End of day one

Day two

Refreshments

Model risk management of non-pricing models

Morning break

Utilizing machine learning for model validation and monitoring of valuation models

Lunch

New techniques for validation of machine learning models

Afternoon Break

Model risk into the future

End of course