Scalability could trump complexity in machine learning debate – Risk.net

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Edited by Bill Coen and D. R. Maurice
By Ermanno Pitacco
Edited by Masha Muzyka, Laurent Birade, Yashan Wang and Jing Zhang
Edited by Per Nymand-Andersen
The debate over the use of more complex and hard-to-explain machine learning-based models to make customer-facing decisions is approaching a tipping point, say senior model risk executives – one that could ultimately extend to more heavily regulated activities such as lending.
Banks have long veered between deploying simpler machine learning techniques that can inform models such as logistic regression analyses, versus those whose computational shortcuts might yield faster results but defy easy
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Largest banks set to win business; others fear regulatory scrutiny of highly concentrated market
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