Featured Speaker: Agus Sudjianto, Executive Vice President, Head of Corporate Model Risk at Wells Fargo

 

Introduced by: Ciro Donalek, Chief Scientific Officer and Co-founder at Virtualitics

 

The banking industry has rapidly adopted Machine Learning for various applications for predictive analytics and process automation. The adoption of AI/ML is very natural because of the nature of the business that is both data and process intensive. While the benefits of AI/ML are very compelling, they are also bringing new risks beyond the traditional financial risk. With their model risk management practice that has been maturing in the last 10 years, banks are ahead of other industries in managing the risk of AI/ML. One of the key aspects to manage the risk is model explainability. While the adoption of so called ExplainableAI, which is typically ‘black box’ machine learning models accompanied by post-hoc explainability tools, is becoming more common for low risk applications, the concern remains for high risk areas such as credit underwriting; thus large banks are typically more cautious in adopting the methodology. There are many recent developments on inherently interpretable, self-explanatory machine learning models without the problem of post-hoc explainers. The focus of my talk will cover applications of AI/ML, their risk management as well as the approach for designing inherently interpretable machine learning for high-risk applications.

 

About Enterprise Data & AI:

The Enterprise Data & AI Community is geared toward innovative companies pushing the boundaries of what’s possible with Artificial Intelligence and cognitive technologies. This community is focused on the enterprise data side of AI including: Data Engineering, Data Preparation, Data Labeling & Annotation, Sourcing and Generating Data, and All Other Topics Data-Related and AI.

 

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Agenda:

  • 11:30-12:30pm: Featured Presentation
  • 12:30-13:00pm: Your Q&A and interaction

Featured Presenters

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