Explainability
Business lines must answer for ML biases – OCC’s Dugan
Banks cannot blame developers or vendors for faulty machine learning models, says regulator
Machine learning governance
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…
Fund houses get picky over where to use machine learning
Buy-siders limit usage of deep learning techniques due to haziness over their inner workings
Not random, and not a forest: black-box ML turns white
Bayesian analysis can replace forest with a single, powerful tree, writes UBS’s Giuseppe Nuti
Making machine learning work for AML
Banks’ anti-money laundering teams are starting to utilise machine learning to combat financial criminals. Risk hosted a webinar in association with NICE Actimize to explore whether these bots can be trusted
Honesty is key to machine learning’s future – Roberts
Oxford-Man Institute director on why tomorrow’s models will gracefully admit defeat
Machine learning hits explainability barrier
Banks hire AI industry experts in face of growing regulatory scrutiny