From Reference Data to Market Integrity: A Control Framework for Security Master Data, End-of-Day Pricing, and Exception Management in Financial Operations

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@article{1719876,
      author = {James Sydney, Lucy Ganyani, Mabasa Masunungure, Munashe Naphtali Mupa},
      title = {From Reference Data to Market Integrity: A Control Framework for Security Master Data, End-of-Day Pricing, and Exception Management in Financial Operations},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {1},
      pages = {2167-2182},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1719876.pdf},
      abstract = {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.},
      keywords = {Security Master Data, Reference Data Management, End-Of-Day Pricing, Data Quality, Market Integrity, Exception Management; Financial Operations, Operational Risk, Controls Monitoring, BCBS 239.},
      month = {July},
      doi = {https://doi.org/10.64388/IREV10I1-1719876}
  }