3rd Annual
Development, Implementation and Management of ML Models
Unlock the transformative impact of AI and ML on operational efficiency and business growth to enable faster and more accurate business decisions and enhance competitive advantages
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How are you ensuring data quality and governance standards are suitably addressed?
Financial institutions need teams to check data quality, do data reconciliations when necessary, and as much as possible have single source of truth. There is also a huge need for documentation including data dictionaries, source tables/archives, and capturing the life cycle of data if revisions are part of the operating model. Finally, timeliness of data is important for meaningful results.
In terms of our event format, (our conferences are informal and intimate peer-led meetings where all speakers and delegates are senior executives from top financial institutions), how do you see it assisting you with overcoming the challenges faced?
Conferences are extremely useful to meet like-minded people in similar areas and for information sharing. There is always something to be learned from another speaker in the industry.
What would you say are the biggest priorities right now with leveraging ML models in finance?
I believe the biggest problems with ML models are the explainability and scaling especially if we are talking about some of the more complex models such as neural networks. Scaling becomes a problem when the underlying data that feeds into the model is massive and full of noise besides potential bias and needs expert judgment.
How should financial institutions approach establishing appropriate data collection frameworks for ML models?
The first step is to determine the relevant sources for data. It might be the case that a combination of structured and unstructured data sources will need to be used and scaling the collection of unstructured data can be problematic. Financial institutions also need to identify internal versus external data sources and the compliance around use of that data. There is a lot of work financial institutions need to do first towards proper data governance and data quality checks before the ML models can work properly and produce accurate results. These include checking accuracy, completeness, timeliness of data besides others such as bias and noise in the data.
Ahead of the GFMI 3rd Annual Development, Implementation and Management of ML Models conference we spoke with Deniz Tudor, Head of Modeling at Bread Financial. Deniz works for Bread Financial in the Treasury department as a senior leader. She also held positions as the Head of Model Development team covering more than 100 models and tools and the Head of Capital, Market, Liquidity, and Model Risk at Bread Financial. Before Bread Financial, she was a Senior Director at Moody's Analytics co-leading the Credit Analytics department. She also worked as a Professor teaching Risk Management at MBA level in San Francisco. She is an experienced leader and strategist specializing in enterprise risk management, economics/scenario planning, compliance/governance, product management, analytics, and AI/ML. She has been working for several years as a thought leader focusing on trends, giving several industry presentations and webinars, and writing many white papers. She has a PhD from UCSD and a MSL (Master's in Law) degree from Fordham University. She also has a graduate level Acct certificate from Harvard and leadership certificates from MIT and Yale.
September 29 - October 1, 2025
New York, NY
An interview with Deniz Tudor, Head of Modeling at Bread Financial
Deniz will be presenting a session during day one (9/30/2025) of the 3rd Annual Development, Implementation and Management of ML Models conference!
For registration pricing and multiple attendee discounts, please contact:
Ria Kiayia
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Interested? Do you feel you will benefit?
Session: Build a robust data collection framework for ML models to enhance data quality at model development stage
- Identify the most appropriate data sources for financial institutions ensuring they meet the needs of ML models.
- Design a scalable and secure data collection infrastructure to gather comprehensive and representative training data.
- Define ethical standards for data collection ensuring privacy and fairness are central to any data gathering process, particularly for sensitive financial information.
- Implement continuous data validation processes to ensure accuracy and relevance across all data sources.
What areas are you looking at over the next 3-6 months in terms of investment for support with your AI and ML model strategies?
A lot of financial institutions are looking into investing in partnerships with platform providers, those that host AI/ML models and there is a lot of need for training and information sharing in regards to LLMs, NLP models, and how to integrate AI for different use cases. We, as an industry, jumped from a push towards automation to AI transformation without a break.
