Bias-free modelling
Models need to be demonstrably free of bias
Model architectures
Exotic, black box model architectures are not acceptable – in fact models need to exhibit a very high level of ‘explain-ability’
Data standards
Data needs to meet rigorous standards in terms of coverage, volume, quality and overall representativeness
Comprehensive project documentation
The project’s documentation, including Minutes of meetings with stakeholders, is submitted to the relevant Regulator for approval
Audit transparency
The overall process is subject to intensive audit at any point in time by the Group Audit function
Deployment documentation
A separate document is often written for deployment and testing
Independent review
A dedicated function (‘Group Risk’ in many banks) reviews and approves the model to exacting standards, providing a rigorous challenge process
Rigorous model selection processes
Selection of a final model needs to be exhaustive and systematic, using a range of methods – and special subsets of the data – to prove that the model will perform well
Stakeholder approvals
A group of key stakeholders need to review and sign-off every intermediate stage of modelling and key artefacts
Modelling process
The modelling process needs to be extensively documented – modellers need to show [1] a strong understanding of advanced parameter estimation, and model diagnostics; and [2] that the model is compliant with the law