Virtual Model Risk Management Summit

INVENTORY
The Many Challenges of Model Inventory

• Brief Review of SR11-7: the US Federal Reserve Board’s comprehensive guidance for model risk management
• Requirements for model inventory articulated by SR-11-7
• Is it a Model or a Tool? Categorizing models into tiers
• Model Risk Management versus Model Development & Ownership
• The three most daunting challenges related to model inventory
• Eleven favorite excuses for ‘Why this model is not really a model.’

Jon Hill, Professor of Model Risk Management, NYU Tandon School of Financial Risk Engineering

INVENTORY
Developing a model risk inventory for high level oversight and management of risk

• Inclusion of qualitative models
• Expansion of model definition
• Regulatory requirements
• Managing a complete inventor

Suman Datta, Head of Portfolio Quantitative Research, Lloyds Banking Group
Ajeeth Sankaran, US Head of Model Risk, Scotiabank
Nikolai Kukharkin, Managing Director, Head of Model Risk Management, TIAA
Jon Hill, Professor of Model Risk Management, NYU Tandon School of Financial Risk Engineering

STRESS SCENARIOS
Reviewing models under stress and analysing capabilities

• Model performance under Covid 19
• Identifying critical paralysis of models
• Increased pressure on models with heightened volatility and uncertainty

Katherine Zhang, Managing Director, State Street
Roderick Powell, SVP, Head of Model Risk Management, Ameris Bank 
Anupam Datta, Professor, Carnegie Mellon University, Co-Founder and Chief Scientist, Truera

MACHINE LEARNING
Risk management for machine learning models

• Key differences between Machine Learning (ML) and traditional risk models, including explainability, stability, overfitting, and unfair bias;
• A risk framework and software platform for ML models that retains important elements of traditional risk management and addresses new risks; and
• Experiences from deploying the platform to evaluate credit models at a major bank.

Anupam Datta, Professor, Carnegie Mellon University, Co-Founder and Chief Scientist, Truera
Shameek Kundu, Chief Data Officer (CDO), Standard Chartered Bank

TECHNOLOGY
The intersection of technology and model risk

• Reviewing goals and outcomes of an effective model risk program
• Data governance, quality and lineage
• Standardisation of model development
• Integration of technology and use of cloud
• Ownership: Modeler vs. data owner
• Calibration and management of the model lifecycle

James Bone, Lecturer-in-Discipline, ERM, Columbia University
Ashutosh Roy, Group Strategic Analytics (GSA) – Lead of Regulation, Internal Governance and Audit Coordination, Deutsche Bank
Rajesh Kaveti, Head of Finance Regulatory/Capital Adequacy Technology/Director, BNY Mellon
Michael Harmon, Managing Director, Market Risk Management, Wells Fargo
Anupam Datta, Professor, Carnegie Mellon University, Co-Founder and Chief Scientist, Truera

General outline of each online session

• Short presentation lasting around 20-30 minutes
• Followed by interactive panel discussion for around 40-45 minutes
• Attendees will have the chance to participate in live Q&A

Each of the days will have its own unique format. For example, there may be during the presentation or panel discussion interactive polls, the thought-leaders may include handouts, whitepaper or additional materials.