This event has passed
Register Here ----> https://bit.ly/2QD4eyt
The proliferation of machine learning has the potential to greatly improve outcomes for consumers, businesses, and other stakeholders across a wide range of applications and industries. While the promise of ML has already been realized, we anticipate the proliferation of ML will increase these benefits throughout various sectors of the economy. Within the financial services industry, lenders’ use of ML tools to measure and identify risk in the provision of credit will likely benefit not only financial institutions, but also the consumers and businesses that obtain credit from the lenders. ML systems’ increased accuracy of assessments of creditworthiness relative to traditional statistical modeling will not only allow lenders to manage risk and earnings more effectively, but will also enable lenders to expand access to credit to communities, individuals, and businesses that were previously unable to access the traditional mainstream financial system.
Join us on Wednesday, September 9th at 11:00 AM CST where we will discuss the considerations for expanding access to credit fairly and transparently and the challenges of governance and regulatory compliance.
- Steven Dickerson, SVP, Chief Analytics Officer, Discover
- Raghu Kulkarni, Vice President - Data Science, Discover
- Nick Schmidt, Director and AI Practice Leader, BLDS LLC
- Patrick Hall, Advisor, H2O.ai & Principal Scientist, bnh.ai
- Ben Cox, Director of Product Marketing, H2O.ai
- Sri Ambati, Founder and CEO, H2O.ai
What you will learn:
- Benefits of machine learning in fair lending
- The difference between disparate treatment and - disparate impact
interpretability and explainability of ML model
- Discrimination testing and mitigation methods