Financial markets rely on a chain of data dependencies that begins with accurate security master data and extends through end-of-day pricing, valuation, trade support, risk reporting, financial control and regulatory accountability. In this chain, a stale price, inconsistent identifier, incorrect asset classification, broken issuer mapping or unresolved vendor discrepancy can propagate across trading, operations, risk and finance processes. This article develops the Reference Data-to-Market Integrity Control Framework (RDMICF), an applied control model for finance operations teams that manage security reference data, pricing-control checks and exception remediation. The framework is grounded in the data-quality and data-governance literature, supervisory expectations on risk-data aggregation, fair valuation and model-risk management, and recent applied scholarship on continuous controls monitoring, AI-enabled audit planning and liquidity-risk analytics. A controlled synthetic operational dataset of 8,010 exception records across 12 monthly review cycles is used to demonstrate the framework's analytical value. The analysis indicates that a disciplined controls architecture could reduce the aggregate exception rate from 23.95 to 11.03 exceptions per 1,000 reviewed records, representing a 53.9% reduction, while lowering SLA breaches from 27.3% to 6.5% and reducing median remediation time from 1.88 to 1.18 days. Heat-map analysis identifies the most risk-sensitive intersections: fixed-income pricing, stale prices, corporate-action adjustments, product taxonomy and security identifiers. The article contributes a practical, auditable and scalable framework for transforming reference-data maintenance from a back-office correction activity into a market-integrity capability that strengthens operational resilience, valuation discipline, data governance and executive decision support.
Sydney et al. (Thu,) studied this question.