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26 Jan 2022 / 17:30 - 18:15
The when, where and how to apply AI in insurance
There are abundant stories about AI creating a new revolution. Nevertheless, general artificial intelligence is currently out of reach and yet we seem to attack every data-related problem through the lenses of deep learning and artificial intelligence.
The old fashion linear regression still has numerous use cases and is widely used in practice, but nobody is talking about it. Despite the prevalent view that deep learning can tackle every problem, artificial intelligence solutions should not be used by default. We discuss attributes of problems amenable to deep learning solutions with emphasis on insurance. Several use cases from insurance will be presented together with underlying models. Deep learning viz-a-viz traditional machine learning is going to be debated with the focus on when to use either one technique.
Diego Klabjan is a professor at Northwestern University, Department of Industrial Engineering and Management Sciences.
He is also Founding Director, Master of Science in Analytics and Director, Center for Deep Learning. After obtaining his doctorate from the School of Industrial and Systems Engineering of the Georgia Institute of Technology in 1999 in Algorithms, Combinatorics, and Optimization, in the same year he joined the University of Illinois at Urbana-Champaign. In 2007 he became an associate professor at Northwestern and in 2012 he was promoted to a full professor. His research is focused on machine learning, deep learning and analytics (modeling, methodologies, theoretical results) with concentration in finance, insurance, sports, and bioinformatics. Professor Klabjan has led projects with large companies such as Allstate, Anthem, Baxter, AbbVie,, and many others, and he is also assisting numerous start-ups with their analytics needs. He is also a co-founder of Opex Analytics LLC, a Coupa company and is now involved with his next start-up.