The Advantages of Model Building in Financial Services

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T. Scott Clendaniel is an Artificial Intelligence Pioneer with 35 years' proven track record of ROI improvements. He’s also a Guest Lecturer at Johns Hopkins University and University of Maryland, Harvard Innovation Labs’ Experfy, Artificial Intelligence course author and the Chief Data Officer of the Board of Directors at Gartner/ Evanta (DC region) 

Episode Links

T. Scott’s LinkedIn: https://www.linkedin.com/in/tscottclendaniel/

T. Scott’s Twitter: https://twitter.com/Strat_AI?s=20 

T. Scott’s Website: https://www.boozallen.com 

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Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:43) – The pace of advancement has changed but problem solving leans more towards software development than problem solving itself.

(03:18) – Deep learning can’t provide solutions unless data is applied beyond the models.

(05:38) – Model building must be fully interpretable to be able to be fixed if needed

(07:15) – Protecting the rights of consumers and increasing the requirements on transparency of the models.

(12:55) – Ethics groups, reviewing policies and the “adverse impact test” for algorithms.

(15:46) –Overestimating AI's impact in the future of work.

(16:49) – Automation and augmented intelligence: humans using computers to solve existing problems, as opposed to being replaced by them.

(21:22) – AI applications in specific industries for specific problems, focusing education on the good and the bad in AI.

(25:10) – Sharing the "wealth of knowledge" about predictive analytics.. 

(27:09) – Open sourcing education so that anyone can learn how to build and use models that are going to impact them.

(31:06) – New research on algorithms to find advanced sophisticated solutions to problems.

(34:07) – Data in general and Artificial Intelligence, specifically, can be used in good ways or detrimental ways.