The bank can generate accurate and reliable risk data for normal and stress reporting, aggregated largely automatically to limit errors. Controls over risk data are as robust as those over accounting data; where manual processes and desktop tools such as spreadsheets and databases are used, effective mitigants such as end-user computing policies and consistent controls are in place; risk data is reconciled to the bank's sources, including accounting data where appropriate; the bank works towards a single authoritative source per risk type; and risk staff have enough access to aggregate, validate and reconcile data to reports. A data dictionary defines concepts consistently across the organisation. Automation and manual judgement are balanced. Supervisors expect all aggregation processes, automated or manual, to be documented, with each manual workaround explained, its criticality described and actions to reduce it proposed; data accuracy is measured and monitored with escalation channels and action plans for poor quality. Expert judgement may fill incomplete data only by exception, without materially affecting compliance, and is documented transparently enough for independent review of the process and criteria.
This control maps to 4 controls across 2 other frameworks. If you already hold one of them, the evidence you collected for it is the starting point here rather than new work.
Every mapping shown was judged rather than inferred from wording similarity, and the ones that failed review are published too. See the coverage reports and what was rejected.
The graph holds this control, the 4 it maps to, and the evidence behind each claim, over MCP and REST.