Financial market operations increasingly depend on the accuracy, timeliness and integrity of the data that underpins trade execution, settlement, regulatory reporting and investor disclosure, yet data-quality failures remain a persistent source of operational and reputational risk. This study examines the impact of data accuracy and quality-control practices on financial market operations and information reliability, situating firm-level survey evidence within the broader trend of rising reported data-quality incidents across Indian financial market intermediaries. A log-linear regression on five years of reported data-quality-incident data (FY 2020-21 to FY 2024-25) confirms significant year-on-year growth (β̂ = 0.2005, t = 13.509, p = 0.0009, R² = 0.984), corresponding to an average annual growth rate of approximately 22.2%. Primary data collected from 90 respondents working in financial-market operations, compliance and data-management roles show that 61.1% experience a data-accuracy issue “often” or “always” in at least one core process, reconciliation and trade-matching is the process most frequently affected, and firms without a formal data-governance framework are significantly more likely to have received a regulatory query or notice (χ² = 4.484, p = 0.034). Chi-square tests of independence further find that firm size is significantly associated with the frequency of data-accuracy issues (χ² = 16.642, p = 0.011), while the verification method used (manual, system-automated, or hybrid) is not significantly associated with reporting-error incidence (χ² = 1.522, p = 0.467). The findings indicate that data-quality risk in financial market operations is real, growing, and structurally linked to firm size and governance maturity rather than to the verification method alone, and the study recommends prioritising formal data-governance frameworks and automated reconciliation controls, particularly for smaller intermediaries.
Dr. Kalicharan Manohar V1 (Wed,) studied this question.