Why should you attend?
Center for Financial Professionals launches its third annual Advanced Model Risk Congress in Midtown Manhattan, New York City. Come and join us on March 12-13, 2024 to alleviate your modeling techniques and enhance your knowledge to take back to your team.
As the technological landscape continues to evolve, it is critical to advance your modeling techniques to ensure best practice in your organization. Advanced Model Risk USA is the go-to congress to build on your professional development with our 7+ hours of networking opportunities and carefully curated agenda of industry thought leaders.
Key highlights
- Black Box Models:
Increasing transparency to understand Black Box methodologies
- Large Language Models :
Aligning Large Language Models with traditional model risk frameworks
- Bias and Explainability:
Manging toxic results of AI/ML and Large Language models
- Quantifying Model Risk:
Enhancing model portfolios to quantify model risk
- Performance Monitoring:
Ongoing performance monitoring to establish model risk management
- Machine Learning :
Developing AI/ML into model risk management programmes
- AI Governance:
Defining best practice of AI/ML models
- Climate Risk:
Defining climate risk into modelling portfolio
Interact with industry front-runners and subject matter experts
Join us for an engaging, timely and carefully curated agenda across 2-days.
Our agenda ensures a deep dive into the timeliest topics of Advanced Model Risk and is explored through presentations, panel discussions, and live Q&A’s to enhance learning
Learn from our line-up of industry thought leaders as they share their expertise.
Become proficient in the topic by homing in knowledge from 20+ subject-matter experts and return to your department with newly developed skill sets and ideas.
Continue conversations with our 7+ hour networking opportunity.
Make most of our 6 networking breaks across the two days plus a complimentary cocktail hour and continue conversations beyond the stage to create meaningful industry connections.
Key speakers
Agus Sudjianto
EVP, Head of Corporate Model Risk
Wells Fargo
Roderick Powell
SVP, Head of Model Risk Management
Ameris Bank
Julia Litvinova
Managing Director, Head of Model Validation and Analytic
State Street
Manoj Singh
Managing Director, Model Risk Officer
Bank of America
Xiangyin (Jane) Zheng
Audit Director
BNY Mellon
Stephen Hsu
SVP, Head of Model Risk Management
Pacific Western Bank
Rodanthy Tzani
Head of Model Risk Management
New York Life Insurance Company
Ankur Goel
SVP, Head of Consumer and Fraud Modelling
PNC
Arthur Robb
Managing Director – Head of Model Risk Management
TIAA-CREF
Janet Shand
Director, Model Risk Management
NYCB
Session previews and related insights
Get an insight of what to expect from the Congress with our past and present speaker session previews.
Balancing digital implementation whilst retaining customer relationships
Balancing digital implementation whilst retaining customer relationships Yawar Choudhry, Head of Client Services Proposition, Legal & General Below is an insight into what can be expected from Yawar's participation at CeFPro's Customer Experience Summit. {{ vc_btn: title=Find+out+more+about+Customer+Experience+Europe&style=outline-custom&outline_custom_color=%23001c64&outline_custom_hover_background=%23001c64&outline_custom_hover_text=%23ffffff&link=url%3Ahttps%253A%252F%252Fwww.cefpro.com%252Fforthcoming-events%252Fcustomer-experience-europe%252F }} The views and opinions expressed in this article are those of the thought leader as an individual, and are not attributed
Ensuring generative AI tools for use within AI applications are used ethically and responsibly
Ensuring generative AI tools for use within AI applications are used ethically and responsibly Dr. John Rates Almasan, Senior Managing Director, Head of ClienTech and AI, TIAA Below is an insight into what can be expected from John's participation at CeFPro's Digital Banking Congress. {{ vc_btn: title=Find+out+more+about+Digital+Banking+USA&style=outline-custom&outline_custom_color=%23d51224&outline_custom_hover_background=%23d51224&outline_custom_hover_text=%23ffffff&link=url%3Ahttps%253A%252F%252Fwww.cefpro.com%252Fforthcoming-events%252Fdigital-banking%252F }} The views and opinions expressed in this article
Anticipating how real-time payments landscape will continue to grow over the next year
Anticipating how real-time payments landscape will continue to grow over the next year Jim Maimone, SVP, Senior Enterprise Payments Platform Product Manager, Citizens Bank Below is an insight into what can be expected from Jim's participation at CeFPro's Digital Banking Congress. {{ vc_btn: title=Find+out+more+about+Digital+Banking+USA&style=outline-custom&outline_custom_color=%23d51224&outline_custom_hover_background=%23d51224&outline_custom_hover_text=%23ffffff&link=url%3Ahttps%253A%252F%252Fwww.cefpro.com%252Fforthcoming-events%252Fdigital-banking%252F }} The views and opinions expressed in this article are those of
Anticipating how real-time payments landscape will continue to grow over the next year
Anticipating how real-time payments landscape will continue to grow over the next year Barry Baird, Head of Payment, TD AMCB, TB Bank Below is an insight into what can be expected from Barry's participation at CeFPro's Digital Banking Congress. {{ vc_btn: title=Find+out+more+about+Digital+Banking+USA+&style=outline-custom&outline_custom_color=%23d51224&outline_custom_hover_background=%23d51224&outline_custom_hover_text=%23ffffff&link=url%3Ahttps%253A%252F%252Fwww.cefpro.com%252Fforthcoming-events%252Fdigital-banking%252F }} The views and opinions expressed in this article are those of the thought leader
Sponsors
To discuss how we can deliver your thought leadership at the event, help you generate leads, and provide you with unique networking and branding opportunities, please contact sales@cefpro.com or call us at +1 888 677 7007 for more information.
Content and media partners
Agenda
8:00 – 8:50
Registration and breakfast
8:50 – 9:00
Chair’s opening remarks
9:00 – 9:45
REGULATION – PANEL DISCUSSION
Enhancing model risk programs to manage divergence in regulatory expectations across jurisdictions
View Session Details
- Restructuring and leveraging existing resources to comply with existing regulatory expectations
- Managing current market environment changes due to bank collapses
- Navigating tighten regulatory expectations
- Enhancing model risk programs to deal with AI regulations
- Different standards of regulations between the US and Europe
- Aligning standards of international banks with US regulations
- Adjusting liquidity balance management models due to new regulation requirements
- Technological expectations to comply with FRTB and Basel 4
- Building capacity, governance, and infrastructure to meet requirements
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Xiangyin (Jane) Zheng, Audit Director, BNY Mellon |
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Oscar Zheng, Executive Director, Head of Model Validation, Natixis CIB Americas |
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Wei Zhu, Managing Director, Citi |
9:45 – 10:20
COMPLIANCE
Defining a model to ensure compliance with regulatory expectations
View Session Details
- Defining a standard for what constitutes a model
- Classifying a clear definition of a modeling tool or solution
- Approaches to dictating if a tool or solution is categorized as a model
- MRM teams vs. SR117’s definitions of a model
- Constituting a model and if it needs to be validated outside of SR117’s guidance
- Managing the challenges and scope of validation requirements
- Managing model risk as a risk and a compliance function
- Looking at model risk beyond a one-size-fits-all all approach
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Janet Shand, SVP & Director, Model Risk Management, NYCB |
10:20-10:50
Morning refreshment break and networking
10:50-11:25
GENERATIVE AI
Understanding advances of generative AI and incorporating into model risk management to mitigate risk
View Session Details
- Looking at AI beyond a model risk management framework
- Increasing training to understand generative AI to fill gaps
- Risk management framework vs. model risk management framework
- Identifying methods to put governance into action for generative AI
- Addressing the multiple tools and business outcomes of generative AI
- Utilizing generative AI to drive efficiency and testing technical aspects
- Risk rating multiple generative AI type models
- Including privacy and legal teams
- Testing the accuracy of AI and generative AI
11:25-12:00
MACHINE LEARNING
Developing an effective AI and machine learning model risk management program
View Session Details
- Increasing number of machine learning models and tools
- Understanding different algorithms and methodologies
- Defining who is responsible for machine learning model risk management
- Data governance in developing machine models
- Learning landscape and following regulatory changes
- Machine learning beyond traditional model risk testing
- Breaking down complexity in methodology, transparency, and technology
- Understanding AI/ML methodologies and non-transparent principles
- Approaching and identifying key risk factors to ensure AI/ML models are fit for purpose
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Aijun Zhang, SVP Machine Learning & Validation Engineering, Wells Fargo |
12:00-12:35
LARGE LANGUAGE MODELS
Adapting traditional model risk frameworks to align with large language models
View Session Details
- Testing and validating large language models
- Developing a forward-look approach to large language models
- Understanding the complexity of generative models for large language model validation
- Creating a platform to implement and develop large language models to mitigate risks
- Managing large language models beyond regulatory reporting
- Building use cases of large language models using NLP
- Controlling large language models whilst leveraging their functions
- Transparency and best practices for risk frameworks
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Agus Sudjianto, EVP, Head of Corporate Model Risk, Wells Fargo |
12:35-1:35
Lunch break and networking
1:35-2:10
BIAS AND EXPLAINABILITY
Assessing and monitoring AI, machine learning, and large language models to avoid bias and toxic results
View Session Details
- Safeguarding models to avoid reputational risk
- Balancing model performance, robustness, and fairness
- Going beyond traditional MRM functions to build models for explainability and bias testing
- Managing uncertainties of acquiring data to ensure models are not biased
- Transparency and explainability analysis within regulatory expectations
- Having a consistent approach to bias and explainability
- Managing stronger requirements for explainability of models in medium-to-long-term investing.
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Roderick Powell, SVP, Head of Model Risk Management, Ameris Bank |
2:10-3:25
BLACK BOX MODELS – PANEL DISCUSSION
Understanding the methodology of black box models and increasing transparency
View Session Details
- Managing black box inputs and outputs
- Machine learning tools outside of vendor black box models
- Developing machine learning models and tools to validate internally
- Validating machine learning models outside of black box models
- Transparency with vendor black box models
- Approaching black box models from a different perspective to further understand
- Having a business approach
- Obtaining documentation and tools from vendors to understand how the model performs well
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Seyhun Hepdogan, Director of Analytics, Fifth Third Bank |
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Moez Hababou, Head of Compliance, CCAR and Credit Models, BNP Paribas |
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Stephen Hsu, SVP, Head of Model Risk Management, Pacific Western Bank |
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Janet Shand, SVP & Director, Model Risk Management, NYCB |
2:55-3:25
Afternoon refreshment break and networking
3:25-4:00
AI GOVERNANCE
Operationalizing governance best practices of AI and machine learning models
View Session Details
- Integrating skill sets and disciplines
- Governing AI beyond a traditional model space
- Dealing with ethics, intellectual properties, reputational risks, and cyber security
- Psychology and linguistic experts to manage limitations of AI governance
- Governing AI beyond use case dependency
- Quantitative tools to measure and manage AI Modeling
- Meaningfully managing and governing AI/ml with model expansion
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Rodanthy Tzani, Head of Model Risk Management, New York Life Insurance Company |
4:00-4:35
MODEL INVENTORY
Managing complexities with the ongoing expansion of model risk scope and inventory
View Session Details
- Putting enhancements in place to accommodate AI
- Using 8 categories of Enterprise Risk Management for managing model governance and inventory
- Considering generative AI in model risk inventory
- Incorporating machine learning models into inventory
- ChatGPT and Chatbots
- Governance of new technologies and advanced models
- Developing replacements for model risk management in inventory
- Addressing the lack of inventory in risk management
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Chris Smigielski, Director of Model Risk Management, Arvest Bank |
4:35-4:45
Chair’s closing remarks
4:45
End of day one and drinks reception
8:00 – 8:50
Registration and breakfast
8:50 – 9:00
Chair’s opening remarks
9:00 – 9:45
GLOBAL VOLATILITY – PANEL DISCUSSION
Managing models with continued volatility and geopolitical challenges and the impact of change
View Session Details
- Using current data to cross reference climate impact on modeling portfolio
- Hurricanes, wildfire risks, flooding risks
- Climate change impacts on ability to provide loans
- Modelling physical and transition risk
- Defining climate risk and constituting where it falls within the model portfolio
- Managing the lack of data to foresee climate stress testing
- Understanding the impact of specific climate stress on the path of macroeconomic variable
- Standards for modeling climate risk
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Alisa Rusanoff, Head of Credit, Crescendo Asset Management |
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Julia Litvinova, Managing Director, Head of Model Validation and Analytic, State Street |
9:45-10:20
CLIMATE RISK –
Reviewing the impact of climate risk and incorporating within model risk management
View Session Details
- Using current data to cross reference climate impact on modeling portfolio
- Hurricanes, wildfire risks, flooding risks
- Climate change impacts on ability to provide loans
- Modelling physical and transition risk
- Defining climate risk and constituting where it falls within the model portfolio
- Managing the lack of data to foresee climate stress testing
- Understanding the impact of specific climate stress on the path of macroeconomic variable
- Standards for modeling climate risk
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C.Robin Castelli, Head of Transition Risk Model Development, Citi |
10:20-10:50
Morning refreshment break and networking
10:50-11:25
FRAUD & FINANCIAL CRIME
Managing the increase in fraud and financial crime tools in model risk management inventory
View Session Details
- Gathering data to govern and review financial crime tools
- Fitting in tools into the traditional definition of models
- Clearly defining financial crime models with regulatory expectations
- Getting appropriate results from quantitative modeling tools
- Evaluating conceptual soundness to ensure the model is fit for use
- Including new dimensions and updating models to capture fraud
- Capturing potential fraudulent events using fraud detection modeling
- Building systems to flag potential threats
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Ankur Goel, SVP, Head of Consumer and Fraud Modelling, PNC |
11:25-12:10
MACROECONOMIC ENVIRONMENT – PANEL DISCUSSION
Understanding how models are measuring interest rate exposure and recalibrating based on market changes
View Session Details
- Identifying key factors of interest rate risk that models need to capture
- Adapting models for emerging risk using historical data
- Adapting model risk processes to identify weaknesses in the framework
- Addressing limitations imposed by regulators and monetary
- Measuring risks accurately and having a sufficient view of measuring interest rate risk
- Incorporating important and significant elements
- ALM – data science problem vs. accounting problem
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Katherine Zhang, Managing Director, State Street |
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Charlie Lu, Former Managing Director, Head of AI/ML Model Risk Management, Barclays |
12:10-1:10
Lunch break and networking
1:10-1:45
QUANTIFYING MODEL RISK
Quantification of model risk and the aggregated model portfolio for end-to-end model risk management
View Session Details
- Assessing the overall risk of an interconnected model network
- Developing a quantitative measurement beyond risk ratings
- Practical and effective ways to quantify model risk
- Understanding the importance of an aggregated model portfolio to mitigate emerging risks
- Intersection and connection point between models
- Verifying and monitoring the data between model
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Manoj Singh, Managing Director, Model Risk Officer, Bank of America |
1:45-2:20
DATA
Managing the evolution of data requirements as model requirements expand
View Session Details
- Incorporating Covid data into modeling
- Controlling covid data
- Updating models using credit loss forecasting models
- Should Covid data be included in these?
- Addressing unpredictable Covid data in the models
- Ensuring models predict credit loss appropriately
- Addressing credit loss due to unprecedented scenarios
- Pandemics
2:20-2:55
VENDOR MODELS
Leveraging vendor models and building out effective oversight capabilities to align with internal governance and controls
View Session Details
- Understanding the risk and controls with third parties and model risk management
- Building relationships with third parties for model risk management
- Having transparency with third parties
- Reviewing vendor documentations
- Developing internal models to mitigate third-party risks
- Capabilities and resources
- Aligning with vendors to mitigate impacts and risk
- Performance measures of vendor models
- Model validation support to ensure new systems meets all requirement
- Managing constant changes from the compliance side
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Hany Farag, Head of Modelling Methodology, CIBC |
2:55-3:25
Afternoon refreshment break and networking
3:25-4:00
PERFORMANCE MONITORING
Strengthening model risk management through ongoing performance monitoring
View Session Details
- Regulatory expectations of ongoing performance monitoring of model risk
- Tracking and validating model performance
- Addressing the thresholds of ongoing model performance monitoring
- Setting tolerance levels on performance
- Incorporating automation in ongoing performance monitoring
- Identifying risks between performance reviews
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Arthur Robb, Managing Director, Head of Model Risk Management, TIAA-CREF |
4:00-4:35
CREDIT RISK
Incorporating emerging credit risks into model risk management frameworks and measuring exposure
View Session Details
- Measuring model exposure to a high inflation environment
- Limitations of models and anticipation of risks
- Adapting concurrent views of model risk to mitigate counterparty credit risk
- Incorporating modeling metrics into daily risk management
- Incorporating overlays into models for credit risk management
- Balance sheet and trading book positioning for decision-making
- Alignment for business changes in the horizon on credit risk
- Developing credit risk modeling strategies
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Daniel Saunders, Head of Model Risk Management, USAA |
4:35-4:45
Chair’s closing remarks
4:45
End of Congress
2024 Speakers
Hear from subject matter experts and industry thought leaders
Senior Executive
Societe Generale tbc
C.Robin Castelli
Head of Transition Risk Model Development
Citi
Hany Farag
Head of Modelling Methodology
CIBC
Ankur Goel
SVP, Head of Consumer and Fraud Modelling
PNC
Moez Hababou
Head of Compliance, CCAR and Credit Models
BNP Paribas
Seyhun Hepdogan
Director of Analytics
Fifth Third Bank
Stephen Hsu
SVP, Head of Model Risk Management
Pacific Western Bank
Julia Litvinova
Managing Director, Head of Model Validation and Analytic
State Street
Charlie Lu
Former Managing Director of Model Risk Management
Barclays
Roderick Powell
SVP, Head of Model Risk Management
Ameris Bank
Arthur Robb
Managing Director, Head of Model Risk Management
TIAA-CREF
Alisa Rusanoff
Head of Credit
Crescendo Asset Management
Daniel Saunders
Head of Model Risk Management
USAA
Janet Shand
SVP & Director, Model Risk Management
NYCB
Manoj Singh
Managing Director, Model Risk Officer
Bank of America
Chris Smigielski
Director of Model Risk Management
Arvest Bank
Agus Sudjianto
EVP, Head of Corporate Model Risk
Wells Fargo
Rodanthy Tzani
Head of Model Risk Management
New York Life Insurance Company
Aijun Zhang
SVP Machine Learning & Validation Engineering
Wells Fargo
Katherine Zhang
Managing Director
State Street
Oscar Zheng
Executive Director, Head of Model Validation
Natixis CIB Americas
Xiangyin (Jane) Zheng
Audit Director
BNY Mellon
Wei Zhu
Managing Director
Citi
Sponsors
To discuss how we can deliver your thought-leadership at the event, help you generate leads, and provide you with unique networking and branding opportunities, please contact sales@cefpro.com or call us on +1 888 677 7007 for more information.
Venue & FAQs
360 Madison Avenue | etc.venues
Madison Avenue
Midtown Manhattan
New York NY 10017
There is no accommodation available at the venue, however, there are plenty of hotels available nearby. To view nearby accommodation based on recommendations by etc.venues, click here.
Frequently asked questions
Frequently Asked Questions
Can I share my thought leadership at Advanced Model Risk USA?
Will there be opportunities to network with other attendees?
- Breakfast, lunch and refreshment breaks
- Drinks reception at the end of day-1
- Q&As, panel discussions, and audience participation technology
What is included within the registration fee?
Where can I find the Congress documentation and speaker presentations?
* Please note that our speakers often have to gain permission from their relevant compliance departments to release their presentations. On rare occasions compliance may not allow presentations to be distributed.
Will breakfast, lunch and refreshment be provided?
Are there any rules on dress code?
Are CPE Credits available?
Register
Register for Advanced Model Risk USA and join the likes of 100+ industry professionals and subject matter experts looking to engage in meaningful conversation and discuss the latest sector developments, trends, and challenges.
Register before to take full advantage of our launch rate special offer.
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