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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