Model risk chiefs warn on machine learning bias

ML model outputs open to “potential bias sitting in your datasets”, says RBS model risk head

Human element: model managers must monitor evolving datasets

Banks’ rapid adoption of machine learning techniques to augment the modelling of everything from credit card approvals to suspicious transactions has left model managers scrambling to make sure their risk frameworks can accommodate them, senior executives are warning.

Banks hope models that make use of machine learning (ML) – a subset of artificial intelligence that relies on automation to create accurate predictions from large, dense datasets – can dramatically speed up manually intensive