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A Conversation With Imir Arifi and Keith Black on Machine Learning Models in Credit Risk Analysis.


Apr 29, 2020 12:34 PM

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Imir Arifi
Trusted AI Evangelist @DataRobot
Dr. Imir Arifi has over 20 yrs of financial market experience, spanning approx. two thirds of that time in quantitative development & validation roles for large financial institutions, and one third reinventing risk management & attribution through ML/AI approaches and currently serves as Trusted AI Evangelist & Product Mgr at DataRobot, providing strategic direction, guidance, and execution to scaling AI & ML solutions in banking & capital market domains. His global client roster includes banks, hedge funds, multi class asset managers, venture capital firms, insurance companies, and financial regulatory agencies. His prior quantitative and ML/AI experience includes credit risk (PD/EAD/LGD), market risk (Var/Large Moves/Prepayments), trading (sentiment analysis/text mining/alpha attribution & pick up/alpha decay), FICC, and others. Dr. Arifi holds a PhD in optimization & credit risk mgmt from the Illinois Inst. of Technology, where he also served as a PT professor of credit risk mgmt.
Keith Black, Ph.D., CAIA, CFA, FDP
Managing Director, Content Strategy @CAIA Association
Dr. Keith Black has over twenty-five years of financial market experience, serving approximately half of that time as an academic and half as a trader and consultant to institutional investors. He currently serves as Managing Director of Content Strategy for the CAIA Association. During his most recent role at Ennis Knupp + Associates, Keith advised foundations, endowments and pension funds on their asset allocation and manager selection strategies in hedge funds, commodities, and managed futures. Prior experience includes commodities derivatives trading, stock options research and CBOE floor trading, and building quantitative stock selection models for mutual funds and hedge funds. Dr. Black previously served as an assistant professor and senior lecturer at the Illinois Institute of Technology.