Adapting to Rising Regulatory Demands and Technological Innovations in Model Risk Management
20+
20+
Sessions Speakers Attendees
Key themes 2025:
Rising Scrutiny
Reviewing the global regulatory and addressing increasing regulatory scrutiny
Geopolitical Risk Implications
Managing global volatility risks in MRM
Ethics and Bias
Addressing gen ai ethical considerations and minimizing bias in model risk
Gen AI Validation
Ensuring model validation for gen ai through governance and automation
Model Validation & Uncertainty
Tackling model validation challenges and managing uncertainty in advanced analytics
AI Risk Practice
AI risk management: Bridging theory and practice
Who’s Participating:
David Palmer Lead Supervisory Financial Analyst Federal Reserve Board
Shawn Tumanov Model & AI Governance Executive GEICO Insurance
Samir Abrol EVP, Chief Model Risk Officer & Head of Data Governance Santander US
Amit Srivastav Managing Director
Morgan Stanley
Deniz K. Tudor Head of Modeling Bread Financial
Agenda | Day 1 | March 4, 2025
8:00 REGISTRATION AND BREAKFAST
8:50 CHAIR’S OPENING REMARKS
Moderated by:
Mehdi Esmail,
Co-Founder & Chief Product Officer, ValidMind
REGULATION
9:00 Optimizing model risk management: Key strategies for compliance, efficiency, and AI integration
Analyze the current regulatory priorities related to MRM
• Identify optimal strategies to enhance audit procedures, ensuring greater efficiency while maintaining compliance
Investigate the most effective set of tools and strategies for managing and validating complex models
• Evaluate the differences in MRM processes required for traditional models compared to AI-driven models
David Palmer, Lead Supervisory Financial Analyst, Federal Reserve Board
RISING SCRUTINY - PANEL DISCUSSION
9:35 Impact of new AI regulations on governance and model risk management
Evaluating global, regional, and state-level AI/ML regulations and their effects on model risk management (e.g., GDPR, CCPA, IFRS, California, New York)
• Assessing regulatory expectations for model risk teams and how they must adapt to new requirements
• Analyzing increased regulatory scrutiny’s influence on compliance, credit risk, and liquidity management
Enhancing model governance to detect credit risk trends early and ensure board accountability
• Exploring alternative and customer data impacts on regulations in the EU/US and financial products like auto loans
• Aligning risk management practices with global, national, and state regulations Florentino Rico, Director for Independent Model Validation, Oportun Amit Srivastav, Managing Director, Morgan Stanley
10:20 MORNING REFRESHMENT BREAK AND NETWORKING
GOVERNANCE
10:50 Governance of AI Applications, including Gen and Model
• Building blocks of AI governance
• Defining a governance structure
Building a robust inventory
• Risk assessment methods
• Testing for bias
Validation approach
Rodanthy Tzani, former Head of Model Risk Management, New York Life Insurance Company
ENHANCING METHODS & PRACTICE – PANEL DISCUSSION
11:20 Adjusting practices to modern challenges and technologies
Identifying gaps in frameworks for assessing risks in AI and Gen AI models
• Addressing new failure modes from model experimentation
• Determining tools and technologies for regulatory scrutiny and operational scaling
Resolving collaboration challenges between model development and risk/ compliance teams
• Incorporating AI tools into model development and validation without increasing risk
Deniz K. Tudor, Head of Modeling, Bread Financial
Tingting She, Head of Model Risk Management, Bank of China New York Branch
Indra Reddy Mallela, Director, Quantitative Finance Manager (Model Risk), Bank of America
12:35 LUNCH BREAK AND NETWORKING
TALENT MANAGEMENT - PANEL DISCUSSION
1:35 Futureproofing talent and training needs for AI models
• Identifying current and future training needs for AI and Generative AI Addressing skill gaps for effective AI model management
Anticipating evolving training requirements in the next 1-2 years
• Comparing skill requirements for AI versus traditional model validation
Reinforcing the need for continuous training to keep pace with AI advancements
Examining successful training programs and comparing centralized versus decentralized solutions
• Training AI teams to identify and mitigate fraud risks from AI-powered bad actors
Chris Smigielski, Model Risk Director, Arvest Bank
Deniz K. Tudor, Head of Modeling, Bread Financial
George Soulellis, Chief Enterprise Model Risk Officer, Freddie Mac
ADAPTING MRM FOR GROWTH
2:20 Integrating MRM programs for small banks with AI applications (crossing the $10b mark)
• Changes in oversight
• MRM as a strategy tool: Improving the effective challenge
Typical new models for small banks
Reducing the budget, bringing validations from external to internal
• Education with model owners and training staff
• Collaborating between departments
Adjusting to AI developments
• Testing black-box AI applications (tools)
• Framework of MRM review for AI applications vs AI models
Pablo Salazar, Director Model Risk Managementr, Stellar Bank
RISKS IN AI - HOW TO CONTROL YOUR ALGORITHMS
Effective AI MRM – Understanding and Managing Algorithmsbias in model risk
Ensuring strong engineering and effective AI
• Overcoming engineering challenges to ensure reliability
•
•
Jos
• Traditional MRM and AI MRM Use cases and practical examples
Gheerargyn, CEO, Yields
3:30 AFTERNOON REFRESHMENT BREAK AND NETWORKING
ETHICS AND BIAS
4:00 Addressing GEN AI ethical considerations and minimizing bias in model risk
Developing strategies to mitigate risks such as model hallucinations
• Reducing hallucinations, bias, and toxicity in LLMs
• Analyzing implications in automated decision-making
Achieving socially responsible outcomes through AI
• Promoting transparency and accountability in machine learning models
• Implementing AI governance frameworks to ensure fairness and accountability
Minimizing ethical risks and biases in financial AI models
Shawn Tumanov, Model and AI Governance Executive, GEICO
Insurance
IMPLEMENTING AND VALIDATING GENAI
4:35 Implementing and validating GenAI: Strategies and lessons learned
Enabling GenAI in customer-facing applications
• Effectively defining, implementing and validating
• Use cases for effective consistency and quality-control
Common challenges faced and effective solutions
Kristof Horompoly, VP - AI Risk Management, ValidMind
GEN AI VALIDATION – PANEL DISCUSSION
5:10 Ensuring model validation for Gen AI through governance and automation
• Reviewing governance practices for validating generative AI models
• Enhancing validation efficiency through automation and risk-tier categorization
Exploring best practices for evaluating and validating AI models
• Addressing hallucinations to ensure reliable model outputs
• Establishing governance frameworks for generative AI use
Managing new failure modes emerging from generative AI experimentation
Oscar Zheng, Executive Director, Head of Model Validation, Natixis CIB Americas
Indra Reddy Mallela, Director, Quantitative Finance Manager (Model Risk), Bank of America
5:45 CHAIR’S CLOSING REMARKS
5:55 END OF DAY ONE AND NETWORKING DRINKS RECEPTION
Agenda | Day 2 | March 5, 2025
8:00 REGISTRATION AND BREAKFAST
8:50 CHAIR’S OPENING REMARKS
Moderated by: Mike
Guglielmo,
Managing Director, Darling Consulting Group
MACROECONOMIC LANSCAPE
9:00 Assessing macroeconomic impacts on model sensitivity and risk
• Assessing the impact of fed rates, inflation, and economic cycles on model sensitivity and risk
• Addressing changing economic environments and unpredictable global volatility
- Reviewing AI-driven financial models
Incorporating macroeconomic data into stress testing and financial forecasting
Alisa Rusanoff, Head of Credit and Technology, Crescendo Asset Management
ANTI-FRAUD AND FINANCIAL CRIME – CASE STUDY
9:35 Refining financial crime models for adaptability to fraud patterns and regulatory changes
• Utilizing biometrics to identify synthetic fraud and identity theft
• Leveraging data and analytics to enhance efficiency
Optimizing performance metrics and sampling for AML and sanctions screening
Ensuring explainability and interpretability of AI/ML models for regulatory compliance
• Deploying methods and strategies to detect and prevent financial crimes enabled by AI technology
• Examining case studies on how AI is used for KYC fraud prevention
• Presenting real-life case studies on fraud schemes and how AI can combat them
Outlining best practices for deploying AI fraud detection systems within highly regulated environments
Chandrakant Maheshwari, FVP, Lead Model Validator, Flagstar Bank
10:10 MORNING REFRESHMENT BREAK AND NETWORKING
MODEL DOCUMENTATION EFFICIENCY – USE CASE
10:40 Optimizing model documentation and automation efficiency
Outlining strategies for comprehensive model lifecycle documentation to support auditing and validation
• Exploring how automation enhances the efficiency of managing model documentation
• Emphasizing the benefits of automated documentation
AUTOMATION - PANEL DISCUSSION
11:15 Enhancing efficiency through automation in model risk documentation, reporting, and validation
Identifying strategies for managing different risk tiers in model validation
• Reviewing tools and methods for automating and categorizing model validation processes
Streamlining model auditing and validation throughout their lifecycle
• Adopting automation to improve efficiency in documentation and reporting processes
Managing and validating third-party models
• Combining automation and best practices to achieve holistic model risk management
Tingting She, Head of Model Risk Management, Bank of China New York Branch
Chandrakant Maheshwari, FVP, Lead Model Validator, Flagstar Bank
QUALITATIVE AND HYBRID RISKS
12:00 Managing qualitative and hybrid risks in model lifecycle management
• Examining best practices for qualitative model risk management and adapting to evolving expectations
• Ensuring governance frameworks align with qualitative risk management needs
• Formulating strategies to manage and mitigate hybrid model risks throughout the lifecycle
• Assessing the impact of changing expectations on qualitative and hybrid risk management practices
Samir Abrol, EVP, Chief Model Risk Officer & Head of Data Governance, Santander US
12:35 LUNCH BREAK AND NETWORKING
OVERFITTING IN AI/ML
1:35 Addressing Overfitting in AI/ML: Effective Detection, Mitigation, and Implementation Techniques
• Exploring the symptoms and root causes of overfitting in AI/ML models
Recognizing methods for identifying overfitting in model performance
• Implementing approaches to effectively reduce and manage overfitting in AI/ML models
Applying practical steps to integrate detection and mitigation techniques into your model management processes
• Reducing overfitting in AML models
Florentino Rico, Director for Independent Model Validation, Oportun
MODEL VALIDATION & UNCERTAINTY – PANEL DISCUSSION
2:10 Tackling model validation challenges and managing uncertainty in advanced analytics
• Evaluating Covid’s impact and incorporating 2020-2023 data into model validation
Integrating behavioral changes from the pandemic into validation processes
• Designing inherently interpretable models and utilizing surrogate models
• Implementing flexible testing and effective risk-tier categorization
Balancing validation with risk management for comprehensive control
• Developing practical strategies for uncertainty management and performance monitoring
Managing interconnected portfolios and enhancing advanced risk measures
Katherine Zhang, Sr. Risk Specialist, Federal Reserve Bank of Boston
Christophe Rougeaux, Model Risk Management Executive, TD
2:55 AFTERNOON REFRESHMENTS AND NETWORKING
AI MODEL MANAGEMENT
3:25 Advancing AI model risk evaluation and validation practice in the era of advanced technology
• Model risk assessment, model risk appetite, model risk mitigation
Digitalization of IV activity and use of GEN AI to enhance validation capabilities
Example of validation of a credit risk model that apply ML/AI techniques
Rita Gnutti, Executive Director- Head of Internal Validation and Controls Head Office Department, Intesa Sanpaolo
AI RISK PRACTICE - PANEL DISCUSSION
4:00 AI risk management: bridging theory and practice
• Measuring and assessing the specific risks associated with AI models
• Applying theoretical concepts to real-world scenarios for effective AI risk management, with a focus on practical applications
• Integrating technological solutions to ensure effective governance and reliability of AI models
Chris Smigielski, Model Risk Director, Arvest Bank
Christophe Rougeaux, Model Risk Management Executive, TD
Dhagash Mehta, Head of Applied Artificial Intelligence Research, Investment Management, BlackRock
4:45 CHAIR’S CLOSING REMARKS
4:55 END OF ADVANCED MODEL RISK USA
Why should you be attending these sessions?
RISING SCRUTINY
Reviewing Global Regulations and Addressing Increasing Scrutiny
Benefits:
• Gain insights into how global regulations are shaping model risk frameworks.
• Learn about emerging trends in regulatory scrutiny and how to prepare.
• Understand how to manage the risks from alternative data usage under current regulations.
AI REGULATION IMPACT
Insurance vs. Banks
Benefits:
• Understand sector-specific challenges in adapting to AI regulations.
• Learn how governance changes impact risk models in both banks and insurance firms.
• Gain actionable insights into aligning model risk management with evolving regulatory demands.
GEOPOLITICAL RISK IMPLICATIONS
Benefits:
• Learn how to incorporate geopolitical risks into model risk frameworks.
• Understand how global regulations and geopolitical tensions affect multinational MRM teams.
• Receive strategies for stress testing and managing cross-border vulnerabilities.
ADAPTING MRM FOR GROWTH
Benefits:
• Practical guidance for integrating MRM as banks scale in size.
• Insights on reducing budget costs through internal model validation.
• Learn how to test and review AI applications in MRM.
ADOPTING AND ENHANCING METHODS & PRACTICE
Benefits:
• Learn how to close gaps in risk assessment frameworks.
• Understand how to handle new failure modes from AI experimentation.
• Discover ways to incorporate AI tools into model validation without increasing risk.
AI MODEL MANAGEMENT
Benefits:
• Learn about cutting-edge tools for automating AI model risk management.
• Gain strategies to improve efficiency and outcomes in MRM.
• Understand the challenges of implementing automation solutions.
ADVANCED RISK METRICS
Benefits:
• Deep dive into advanced stress testing methodologies.
• Learn how to incorporate extreme VaR and AI into model risk frameworks.
• Gain insights on updating AI models with Covid-era and other volatile data.
GEN AI VALIDATION
Benefits:
• Understand governance strategies for effective Gen AI model validation.
• Learn how automation can streamline validation processes.
• Gain insights into addressing hallucinations and experimental risks in Gen AI models.
Sponsorship & Partnerships
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Networking
Whether over coffee, lunch, drinks reception, or dinner, expand your network connections in person.
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Co-sponsors
2025 Speaker Line-up
Samir Abrol VP, Chief Model Risk Officer & Head of Data Governance Santander US
Jos Gheerargyn CEO Yields
Mehdi Esmail Co-Founder & Chief Product Officer ValidMind
Rita Gnutti Executive Director- Head of Internal Validation and Controls Head Office Department Intesa Sanpaolo
Dhagash Mehta Head of Applied Artificial Intelligence Research, Investment Management BlackRock
Christophe Rougeaux Model Risk Management Executive TD
Chris Smigielski Model Risk Director Arvest Bank
Shawn Tumanov Model and AI Governance Executive GEICO Insurance
David Palmer Lead Supervisory Financial Analyst Federal Reserve Board
Alisa Rusanoff Head of Credit and Technology Crescendo Asset Management
George Soulellis Chief Enterprise Model Risk Officer Freddie Mac
Rodanthy Tzani former Head of Model Risk Management New York Life Insurance Company
To view the full Advanced Model Risk USA 2025 speaker biographies scan the QR code or click here
Kristof Horompoly VP - AI Risk Management ValidMind
Indra Reddy Mallela Director, Quantitative Finance Manager (Model Risk) Bank of America
Pablo Salazar Director Model Risk Management Stellar Bank
Amit Srivastav Managing Director Morgan Stanley
Katherine Zhang Sr. Risk Specialist Federal Reserve Bank of Boston
Chandrakant Maheshwari FVP, Lead Model Validator Flagstar Bank
Florentino Rico Director for Independent Model Validation Oportun
Tingting She Head of Model Risk Management Bank of China New York Branch
Deniz K. Tudor Head of Modeling Bread Financial
Oscar Zheng Executive Director, Head of Model Validation Natixis CIB Americas
Convince your Boss
#1 What Your Boss Will Say: “What’s included within the ticket price?”
“For the price of my ticket, I’ll gain full access to both days of CeFPro’s Advanced Model Risk Congress, which offers over 8 hours of dedicated networking with industry leaders, C-suite executives, and peers, all within structured breakfast and lunch breaks alongside a dedicated drinks reception at the end of day one.
Beyond networking, the learning continues with exclusive post-event materials, resources, and a personalized CeFPro Connect portal, granting me access to the latest intelligence and trends in risk management to enhance my work long after the event.”
#2 What Your Boss Will Say: “Will you learn anything of value that we can integrate into our strategy?”
“The agenda for this event has been thoughtfully crafted based on insights from over 25 research calls with high-level model risk experts representing a diverse range of financial institutions. It’s designed to tackle the real challenges and opportunities that senior practitioners are addressing in their own strategies right now.
These sessions will give me practical insights and the latest advancements in model risk management that I can apply directly to strengthen and innovate our operations. The knowledge I gain will help our team refine our approach, uncover new opportunities, and tackle emerging challenges in the field.
Below is a breakdown of the seniority of the speakers who will be sharing insights at our Advanced Model Risk Congress.”
2 2 1 1
#3
What Your Boss Will Say: “What specific benefits will attending this event bring to our team?”
“This event presents a great opportunity for team building and development, with sessions covering critical topics like Regulation, Talent Management, Ethics & Bias and Gen AI Validation. There are group discounts available, making it easier for us to bring the whole team together to dive deep into these subjects and discuss how we can apply what we learn during breaks.
If I attend alone, I’ll still have access to post-event materials and resources that I can share with the team upon my return. I can also direct them to CeFPro Connect, where they can create free accounts to access additional resources.
Whether I attend with colleagues or by myself, there will be over 8 hours of networking with industry leaders, allowing us to gain valuable insights that we can apply in our work.”
#4 What Your Boss Will Say: “What will we do with you out of the office for 2 days?”
“The venue will have Wi-Fi, so I can bring my laptop if needed. There are also plenty of breaks for lunch and refreshments, giving me the chance to step out and support the team if necessary.
Having at least one of us attend this event will provide valuable insights for our department and contribute to our overall understanding of the industry. This makes it a worthwhile investment of my time. Plus, the extended learning opportunities available after the event will ensure that the benefits continue long after I return.”
#5 What Your Boss Will Say: “How will you share the knowledge and insights gained with the rest of the team?”
“I’ll be able to take notes during the sessions to capture key takeaways and points for us to consider. If you’d like, I can prepare a presentation or report on my findings and recommendations to share everything I learn.
Additionally, I’ll have access to post-event materials, including copies of the presentations, in-depth interviews with the speakers, and related articles and videos. I can share these resources with the team to reinforce our discussions and insights.”
For further help in convincing your boss to let you attend, Scan the QR code or click here for access.
Venue & Location
Times Square
A vibrant hub, known for its bright lights, theaters, and entertainment options, perfect for experiencing New York’s lively atmosphere.
The Capital Grille
An upscale steakhouse featuring dry-aged steaks, fresh seafood, and an extensive wine list, offering a refined atmosphere for team bonding.
New York Bar Association, 42 West 44th Street, New York, NY 10036
Wall Street
Just a short subway ride from Midtown, Wall Street is the heart of the financial district and home to the NYSE.
Nearby Hotels
The Museum of Modern Art (MoMA)
A premier art museum showcasing modern and contemporary masterpieces, making it an inspiring cultural stop near the Bar Association.
Booking a hotel in Midtown Manhattan for the event ensures you’re right in the heart of the action, making it convenient to attend every session, explore local amenities, and soak up the vibrant city atmosphere without the hassle of commuting.
• The Algonquin Hotel
• The Knickerbocker Hotel
• The Chatwal
• Marriott Marquis
Registration
Launch Rate
December 13
Early Bird Rate
Janurary 31
Standard Rate
After January 31
*For those representing a financial institution/government body
Group Rates
Seize the opportunity, bring the team to advance their professional development and knowledge with our group booking promotion.
50% OFF:
Purchase two tickets and receive the third registrant at 50% off the prevailing rate
Free Pass:
Don’t stop there, as the more people you register, the better the savings. With every four tickets bought, the fifth is on us, completely free!
Bringing your team not only enhances the overall experience, but also fosters significant team building among colleagues while allowing you to save on your registration.
What’s Included
Access to 20+ sessions
Networking: 3+ hours
Lunch + Refreshments
Networking cocktail reception
PPT slides/decks
Podcasts with industry experts
Videos and interviews from the event
Connect Magazine complimentary
CeFPro Connect membership
Community network and engagement
Market intelligence reports access
To register your place at the best rate possible, click here, or scan the QR code.
Topic Related Insights
Developing financial crime modeling approaches to more advanced capabilities
Behrouz Raftari Tangabi, Model Validator, ING, & Suvadeep Adhikari, Senior Data Science Model Validator, ING
Transaction monitoring is key to tracking and analyzing financial transactions to detect and prevent financial crime. Rule-based models are a common approach, but how can more advanced capabilities be used to better the model process? At CeFPro’s Fraud and Financial Crime Europe 2023 Summit, Senior Model Validators from ING discussed how new technologies can advance modeling approaches.
Transaction monitoring is the process of tracking and analyzing transactions to detect and prevent financial crime. The approach operates under the premise that unusual or suspicious transactions can highlight crimes such as money laundering and terrorist financing. Therefore, financial institutions must accurately scrutinize transactions to identify potential threats and take appropriate action.
It is known that operational costs, timeconsuming investigations, reduced efficiency, missed true positives, and reputational risk are all factors as to why transaction monitoring is a challenge.
Rule-based models represent a common approach in transaction monitoring, relying on predefined sets of rules, often formulated as “if-then” statements, to make decisions or
predictions about the legitimacy of a transaction. While rule-based models have advantages, such as simplicity and explainability, they have several disadvantages. When asking Behrouz Raftari Tangabi prior to the session, “What are the key challenges with regards to rule-based models?” He said:
“Rule-based models are not flexible as they rely on predefined conditions, proving challenges in capturing complex patterns. This can be even more challenging in a dynamic environment where adaptability is needed, for example, in fraud cases. Maintaining the effectiveness of rule-based models can be a heavy and timeconsuming exercise, especially when the number of rules grows. The modification of rules or adding new ones can also be challenging when there are dependencies or interactions between rules. As rule-based modeling heavily relies on expert knowledge, it requires continuous involvement from domain experts.”
To continue reading click here, or scan the QR code.
Setting thresholds for rule-based models can be a complex exercise. Financial institutions must strike a delicate balance between being vigilant and not inundating investigators with excessive alerts.
Topic Related Insights
Challenges, Methodologies, and the Impact of Macroeconomic Factors on Credit Risk Management
Varun Nakra, VP Credit Risk Modelling, Deutsche Bank
You’re participating in a panel discussion at CeFPro’s Credit Risk USA event on September 25 and 26, where you’ll be looking at a number of risk dimensions, including modeling.
Can you run through some of the key methodologies and statistical techniques that you’ve used over the course of your career and currently used and how you ensure those models remain accurate and relevant in an economic environment that’s constantly changing.
Well, to begin with, the techniques and methodologies depend on the quantitative problem at hand, so there’s no cookie cutter or ‘one size fits all’ approach.
However, there is a bread-and-butter logistic regression which is being used across all the models inside and outside of credit risk. But it really just depends on what we’re modeling and what the end objective is.
For instance, a short run point in time probability of default (PD) model is pretty much built like a scorecard using regression and following modeling methodology or steps such as rates of evidence, information value, ensuring the model is balanced with respect to the factors included in the model.
But if you want to convert the short run PD model to a long run PD model, which is a true cycle PD model, then the methodology will change. It will depend on the data availability, time and resources.
As an example, one of the approaches could be to estimate a central tendency using a vector autoregressive approach, and we include some macroeconomic variables in the model, so we simulate all possible scenarios.
We can then say we had a short run PD as an input to the model, but we converted it into a ‘through the cycle’ PD which is an average over different macroeconomic scenarios.
In a nutshell it all depends on what we’re modeling – PD, LGD or traditional scorecard – and we have to customize our methodology for that end objective.
To continue reading click here, or scan the QR code.
The models remain accurate and relevant for a certain length of time, but when they stop being accurate you need to redevelop the model or recalibrate the model for one or more of several possible reasons.
Great minds think alike, but brilliant minds think differently.
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