Course Agenda

Agenda

Model Risk Management - London

Day 1 - Wednesday, November 6, 2019

08:30

Registration and refreshments

09:00

The regulatory landscape and governance of model risk

  • Practical insights into establishing a model risk management function 

  • Evolving the definition of models to encompass AI and ML

  • UK vs EU vs US, EBA, CRD V, Basel, ISDA, TRIM

  • Governance – lines of defence

  • Model risk owner vs model owner

  • Implementing governance around the models

  • Capturing model interconnectivity

  • Responsibility for development, evaluation & documentation

10:30

Morning break

10:45

Building a model risk management framework

  • Core elements of a MRM framework

  • MRM governance: master plan, scope and deliverables

  • MRM organization: lines of defense, MR function and the model owner role

  • MRM policies and procedures: MRM policy, model definition, model risk appetite and model tiering 

  • MRM tools: inventory, workflow and reporting

  • Regulatory expectation 

  • Link to TRIM and BCBS239 and MRM and machine learning

  • Quantifying model performance

12:00

Lunch

13:00

Model risk management for stress testing

  • Interconnectedness of models and network effects

  • Stress testing and the P&L distribution

  • Sensitivity tests

  • Bayesian averaging of the models’ outcomes

  • Technology bottlenecks

14:30

Afternoon break

15:00

Model risk perspective on FRTB and LIBOR transition

  • LIBOR transition and FRTB: impact on the risk modelling

  • Estimating risk factors: the importance of data

  • FRTB: NMRFs and P&L attribution test

  • LIBOR: alternative RFRs

  • Impact of effectively managing and delivering FRTB and Libor program schedules in parallel

16:30

End of day one

Day 2 - Thursday November 7, 2019

08:30

Refreshments

09:00

Model risk management for pricing models

  • Market of products vs. pricing and hedging

  • Establishing pricing and validation framework

  • Industry approach to pricing models

  • Source of valuation adjustments in pricing

  • Identification and mitigation of model and input risk

10:30

Morning break

11:00

Model risk management of credit models

  • IFRS 9, CECL, LGD estimations

  • Aligning methodology and models

  • Role of a credit risk model and credit spread output

  • Addressing cyclicality

  • Structural vs reduced form models

  • Default intensity models

12:00

Lunch

13:00

Auditing model risk management

  • Role of internal audit

  • Common weaknesses in model risk management

  • What is a good control environment?

  • Should audit duplicate the role of model risk management? 

14:30

Afternoon break

15:00

Machine learning for model validation 

  • Why banks need larger validation throughput and how to use AI to speed up

  • Assessing 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)

16:30

End of course