Reducing Supervisory Risk through Validation Frameworks: Designing Preventive Controls for CCAR Edit Checks and Data Quality Failures
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V7I1P117Keywords:
CCAR Validation, Supervisory Risk, Edit Checks, Data Quality Controls, Supervisory Risk Controls, Regulatory Reporting Governance, Audit DefensibilityAbstract
Supervisory risk in Comprehensive Capital Analysis and Review (CCAR) programs increasingly arises not from capital inadequacy, but from weaknesses in data quality, validation execution, and the late discovery of reporting defects through downstream edit failures [1], [2]. Federal Reserve examinations repeatedly highlight deficiencies where institutions rely on post-aggregation reconciliation, manual review, or cycle-end remediation to identify issues that could have been prevented earlier in the reporting lifecycle. This paper examines preventive validation as a distinct supervisory risk control discipline and proposes a validation framework that embeds data quality enforcement and CCAR edit checks directly into reporting execution. Drawing on practitioner experience across CCAR, capital reporting, and regulatory validation programs, the paper introduces a preventive control model that shifts validation upstream, integrates supervisory intent into edit logic, and produces audit-ready evidence as part of normal processing. By treating validation as supervisory infrastructure rather than a post hoc quality function, large banking institutions can materially reduce supervisory risk, minimize repeat findings, and improve the sustainability of CCAR reporting outcomes.
References
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