Machine learning for risk calculations : a practitioner's view

Abstract

"The computational demand of risk calculations in financial institutions has ballooned. Traditionally, this has led to the acquisition of more and more computer power -- some banks have farms in the order of 50,000 CPUs, with running costs in the multimillions of dollars -- but this path is no longer economically or operationally viable. Algorithmic solutions represent a viable way to reduce costs while simultaneously increasing risk calculation capabilities