I
have been writing about the new and the old in risk management over the past
year. This starts with the slow pace of adoption of FinTech by incumbents in
financial services. I have suggested that an important component of the change
needed includes incumbents amending and enhancing risk management frameworks to
reflect new FinTech innovations. (See my last post on
the subject.)
Recently,
I came across an article from
McKinsey
that makes a similar point in the context of model risk and the adoption of artificial
intelligence (AI) and machine learning. It turns out I am in good company!
McKinsey’s
article notes that banks have developed and implemented frameworks to manage
model risk, including model validation reflecting specific regulatory
frameworks, in this case from the US Federal Reserve (here). They recognise
that the implementation of these frameworks is not appropriate to deal with the
model risk associated with AI and machine learning. Banks are therefore proceeding
cautiously and slowly introducing new modelling approaches even when these are
available.
The
article then shows how a standard framework for model risk management is used
to identify extra considerations required for this
framework to cover appropriately AI and machine learning models. The key message is that the challenge of adopting
AI and machine learning can be addressed through a careful consideration of
existing approaches.
Two
further thoughts from McKinsey’s article. Firstly, the article rightly refers
to model management rather than validation. It is always useful to reiterate
that model validation undertaken by the risk function is just a component of
how models are managed in the business. Secondly, model management should not
apply only to internal models used to calculate regulatory capital, but should
apply more widely to models used in the business such as those used for
pricing, valuation of assets and liabilities.
The
article ends with a cautionary tale of an unnamed bank where the model risk
management function took initial steps to ready itself for machine learning
models on the assumption that there were none in the bank. It then discovered
that an innovation function had been established and was developing models for
fraud detection and cybersecurity.
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