Scalability could trump complexity in machine learning debate

Risk USA: banks “on the precipice” of adopting more complex models, says Goldman exec

quantum computing explainability

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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Calibrating interest rate curves for a new era

Dmitry Pugachevsky, director of research at Quantifi, explores why building an accurate and robust interest rate curve has considerable implications for a broad range of financial operations – from setting benchmark rates to managing risk – and hinges on…

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