Effectively develop and govern machine learning models for appropriate implementation and to help overcome issues surrounding data and explainability.

The potential of machine learning (ML) for model development has grown vastly over the last few years and financial institutions need to ensure that they are able to capitalize on the significant benefits of cost and time reduction that ML can offer, and gain every competitive edge they can. Moreover, the expected rise in regulation around the use of this technology as a result of it being implemented more widely across the financial industry makes it critical for firms to develop a clear understanding of what this means for their practices and gain the practical knowledge required, to ensure their models remain compliant. Lastly, as ML models require a high level of investment and expertise, it is crucial for financial institutions to implement them appropriately to secure a high ROI from this investment.

The GFMI Development, Implementation and Management of ML Models conference will offer case studies on how financial firms have overcome challenges when applying machine learning in model development. The significant challenges that will be addressed will be data bias and explainability. Experts in the field will discuss solutions such as the introduction of hyperparameters, the utilization of traditional models and ensuring adequate infrastructure to monitor machine learning models is in operation. The focus will also be drawn to ensuring attendees gain practical knowledge to ensure that their models will fall in line with regulations, allowing for the establishment of more robust and accurate models.  

Attending This Premier marcus evans Conference Will Enable You to:

• Identify the appropriate context of when to deploy machine learning for model development 

• Analyse the best practices to manage and cleanse data

• Examine why issues of explainability and interpretability often occur for model development teams and learn how to combat these challenges

• Optimize machine learning compliance with regulation

• Investigate the necessary frameworks that need to be developed for machine learning models

• Explore how traditional models can co-exist alongside machine learning models

Best Practices and Case Studies from:

• Arthur Maghakian, Managing Director, Data Science and Machine Learning, Goldman Sachs

• David Wang, Managing Director, Artificial Intelligence and Financial Engineering, State Street Corporation

• Surnjani Djoko, Senior Vice President, Specialized Analytic Group Manager, Citi

• Stefan Szilagyi, Model Risk Examination Manager, Federal Housing Finance Agency

• Nengfeng Zhou, Senior Lead Quantitative Analytics Consultant, Wells Fargo

• Ankur Goel, Senior Vice President, Head of Consumer Modeling, PNC

For more information and registration discounts please contact: Ms Ria Kiayia, Digital Media and PR Marketing Executive at riak@global-fmi.com or visit: https://bit.ly/3DRSnWx