About this paper

If the early evidence from recent reviews is anything to go by, banks still have significant work to do to get their modelling practices in order.

This has subsequently meant a compliance headache for banks, and a huge spend on hiring or redeploying quants from model development to risk management and validation teams. Quants don’t come cheap, nor do the army of consultants brought in to oversee the process. Sources tell tales of one US bank that attempted to lower costs by cutting as many PhD model quants as it could, and replacing them with master’s graduates – only to be red-flagged by its regulator.

Meanwhile, the ability of machine learning models to read great quantities of unstructured data, spot patterns and translate it into actionable information is driving a significant uptake in the technology. David Asermely, SAS MRM global lead, highlights the need for rigorous model governance as businesses expect to adopt artificial intelligence and machine learning models to support key risk business use cases.