


To maximize engagement and interaction, seats for the course will be limited and available on a first come, first served basis – to avoid disappointment, book your place today by clicking on our register tab.

Nate Vanderheyden
Vice President, U.S Banks Cyber & Information Security
Morgan Stanley

Yogesh Mudgal
Operational Risk – Global Head of Enterprise Tech/Cyber Architecture &
Engineering Risk
Citi
Agenda
Registration and breakfast
Chair’s opening remarks
CYBER CAPABILITIES
Reviewing the evolution of cyber threats and technology capabilities to stay ahead
Increased sophistication of threat actors
Keeping ahead of advancing tactics
Developing capabilities to recognize threat actor activity
Sharing threat intelligence across the industry
Increased risk with economic uncertainty and global tensions
Evolution of incentive from financial to disruption
Increased use of NLP impact to threat
Reviewing progress in quantum computing
DIGITAL ID
Managing increased digital threat and protection of digital identity with increased social engineering capabilities
Countering cyber enabled threat actors
Increased vulnerability with digital advances
Evolution of approaches
Multichannel outreach attempts
Enabling detection and prevention capabilities
Advancing endpoint protection software
Implementing detection and prevention controls
Understanding user behaviors to identify anomalies
Morning refreshment break and networking
RESILIENCE
Vulnerability management and response tactics to drive cyber resilience
Patching requirements
Managing volume of threats
Resource management to stay ahead of change
Daily threat intelligence
Impact of Russia/Ukraine conflict on threat landscape
Responding to evolution of threats
Developing situational awareness
Leveraging threat based security monitoring
Enhancing incident response and crisis management practices
Opportunities with fully automated response technology
Developing and enhancing cyber resiliency
SUPPLIER RISK
Protecting the organization from external threats and ensuring due diligence and
oversight of all suppliers
Managing third party/supplier incident
Effective due diligence on suppliers
Managing risk leveraging vendor services
Interaction and engagement with third parties
Cybersecurity assessment of vendors
o Data protection programs
Evidencing controls and security at RFP stage
Benchmarking acceptable levels of security
Understanding interdependencies across supply chains
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Nate Vanderheyden, Vice President, U.S Banks Cyber & Information Security, Morgan Stanley |
Lunch break and networking
CLOUD SECURITY
Managing concentration risks with increased use of cloud services and ensuring resilience of security measures
Developing a risk-based approach
Reviewing breaches within public cloud infrastructure
Migrating and leveraging large cloud service providers
Developing trust in security measures
Infrastructure and maintenance reassurances
Misconfiguration risks
Continual assurance and data validation
Vulnerability management tests and assessments
Integrating cloud security processes with existing security processes
Recognizing cloud vulnerabilities
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Yogesh Mudgal,Operational Risk – Global Head of Enterprise Tech/Cyber Architecture & |
INTELLIGENCE
Leveraging intelligence and converting into actionable metrics through public and private partnerships
Delivering enterprise level intelligence and translating to the board
Quantitative and qualitative measurement
Developing measurement frameworks
Understanding intelligence inputs and outputs
Increased intelligence sharing with continued geopolitical unrest
Working with federal government in an incident
Requirements for responses to government bodies in cyber evento Identifying proper response to an incident
Responses to incidents to mitigate impact
Afternoon break and networking
PEOPLE RISK
Attracting and retaining the best cybersecurity talent
Education teams on the importance of security
Understanding new threats facing the organization
Instilling speculation in teams
Balancing training and disciplinary action
Educating on downstream impacts
Instilling cybersecurity hygiene
Implementing a strong cybersecurity culture
Effectiveness of training beyond single day
Modifying job descriptions and scope of degrees
Identifying untapped sources of talent
RANSOMWARE
Reviewing approaches and response to ransomware attacks and mitigation techniques
Managing disruption of ransomware attack
Evolution of ransomware variants
Response approaches to attacks
Protecting data after an attack
Reviewing whether ransom should be paid
o Regulatory and legal implications
Protecting reputation through risk mitigation
End of Masterclass

Speakers
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8:15 Registration opens
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9:00 The Masterclass will begin
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We aim to conclude the Masterclass around 5:00
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Refreshments will be available throughout the day
8:15:
Registration opens
9:00:
The Masterclass will begin
5:00:
We aim to conclude the Masterclass
:
Refreshments will be available throughout the day
To maximize engagement and interaction, seats for the course will be limited and available on a first come, first served basis – to avoid disappointment, book your place today by clicking on our register tab.

Nicholas Schmidt
CEO
SolasAI

Nicholas Schmidt
Nicholas Schmidt is the CEO of SolasAI, a compliance-focused AI software platform that identifies and mitigates bias and discrimination in algorithmic decisioning. He is also the Artificial Intelligence Practice Leader at BLDS, LLC, where he provides expert guidance in the application of economics and statistics to questions of law and regulation. As head of the AI practice, Nick focuses on algorithmic fairness, explainable AI, and ensuring robust model governance practices. In addition to working with many of the largest U.S. lenders, FinTechs, and insurance companies, Nick regularly advises regulatory agencies in addressing questions relating to discrimination and innovation in AI.

Vijay Nair
Head of Advanced Technology for Modelling
Wells Fargo

Vijay Nair
Vijay is speaking at Machine Learning Model Validation Masterclass

Agus Sudjianto
Head of Corporate Model Risk
Wells Fargo

Agus Sudjianto
Agus Sudjianto is an Executive Vice President and Head of Corporate Model Risk for Wells Fargo where he leads a highly technical team to manage model risk across the enterprise. Prior to his current position, Agus was the Modeling and Analytics Director and Chief Model Risk Officer at Lloyds Banking Group in the United Kingdom where he was responsible for the enterprise development and oversight of all risk management models (Retail and Wholesale Credits, Market, Regulatory Capital, Stress Testing, Asset Liability Mangement, Insurance). Before joining Lloyds, he was a Senior Credit Risk Executive and Head of Quantitative Risk at Bank of America. Prior to his career in banking, he was product design manager at Ford Motor Company where he led engineering teams designing engine systems and components using complex engineering models. Agus holds numerous US patents in both Finance and Engineering fields. In addition to publishing numerous technical papers, he is also a co-author of a statistics book in Design and Analysis of Computer Experiment. His technical expertise and interest include Quantative Risk, especially credit risk modeling and statistical finance, statistical methods for fighting financial crimes, and computational statistics. He holds graduate degrees in Engineering and Management from Wayne State University and Massachusetts Institute of Technology.
The focus of this workshop is to provide the latest development in model validation for Machine Learning with special emphasis on evaluation of conceptual soundness and
outcome analysis. Key topics for conceptual soundness includes model causality, explainability and interpretability, as well as dealing with over-parametrerized/under-specification problems commonly occurred in machine learning models. Designing inherently interpretable machine learning models will be discussed in-depth considering the importance of the methodology for high-risk applications as well as its role for model benchmark. Outcome analysis will cover advanced topics beyond the standard performance analysis such as: identification of model weakness through error slicing, prediction reliability (conformal prediction), model robustness, model resilience under changing environment and model fairness.
Session 1:
Introduction of ML Model Validation: Conceptual Soundness
Introduction to ML and ML Model Validation
Feature Selection and Causality
Post-hoc Explainability
Session 2:
Inherently Interpretable Models
Deep ReLU Networks as Locally Interpretable Models
Exact interpretability for Deep ReLU Networks
Functional ANOVA and Globally Interpretable Models
Constructing Explainable Boosting Machine
Session 3:
Outcome Analysis: In and Out of Distribution Testing
In Distribution Testing: Model weakness and Reliability
Evaluating Reliability of Model Prediction using Conformal Prediction and Slicing
Out of Distribution Testing: Robustness and Resilience
Evaluating Model Robustness against Input Corruption
Session 4:
Model Fairness
Model Fairness and De-biasing
Group Fairness Metrics and Their Calculation: Independence, Separation and
Sufficiency
Methodology for Bias Mitigation
Additional Hands-On Exercise and Wrap Up