The Supervisory Data Quality Index (sDQI)

Source: BS

Summary

• Overall SCB Score: 90.9 (Improved from 90.7).
• Best Performing Group: Small Finance Banks (91.9).
• Group with a Dip: Public Sector Banks (91.0).
• Improving Metrics: Accuracy and Consistency.
• Declining Metrics: Completeness and Timeliness.

Context:

The Supervisory Data Quality Index (sDQI) is the Reserve Bank of India’s (RBI) primary metric for measuring how reliably banks report their financial health. As of December 2025, the overall index for Scheduled Commercial Banks (SCBs) has shown a marginal improvement to 90.9, signaling that while Indian banks are becoming more accurate, they are still facing “bottlenecks” in getting the full data to the regulator on time.

The Four Pillars of sDQI

For the RBI to maintain financial stability, the data it receives must be “Supervisory Grade.” The sDQI evaluates every bank submission based on four distinct criteria:

• Accuracy: Does the data reflect the true financial state (e.g., are NPA numbers exact)?
• Consistency: Is the data uniform across different reports (e.g., does the balance sheet match the sectoral credit report)?
• Completeness: Are all mandatory fields filled out, or are there “data gaps”?
• Timeliness: Was the report submitted within the strict regulatory deadline?

Performance Trends: The “Quality vs. Speed” Trade-off

The December 2025 report highlights a diverging trend in how banks manage their data governance:

• The Gains: Banks have significantly improved their Accuracy and Consistency. This suggests that internal automated data flows are working well to ensure the numbers are “correct.”
• The Lags: These gains were partially offset by a decline in Completeness and Timeliness. This indicates that while the data is correct, banks are struggling to compile the entire dataset and submit it within the prescribed windows.

Sectoral Winners and Losers

The sDQI scores reveal a clear hierarchy in data governance across different banking groups:

Strategic Significance for the RBI

High-quality data is not just a clerical requirement; it is a Risk Management tool.

• Early Warning Systems: Accurate data allows the RBI to spot a rise in bad loans or liquidity stress before it becomes a crisis.
• Policy Calibration: If data is inconsistent or late, the RBI’s Monetary Policy Committee (MPC) might make decisions based on outdated or “incomplete” information.

Conceptual MCQs

Q1. Which of the following parameters saw an aggregate improvement across Scheduled Commercial Banks (SCBs) in the December 2025 sDQI report?

A) Completeness and Timeliness

B) Accuracy and Consistency

C) Only Timeliness

D) All four parameters equally

Q2. Small Finance Banks (SFBs) achieved the highest sDQI score (91.9). Which specific areas contributed to this perfect sub-score?

A) Timeliness and Completeness

B) Internal Audit and Human Resources

C) Accuracy and Consistency

D) Loan Recovery and NPA Management

Answers:

• Q1: B (Report states accuracy/consistency improved while completeness/timeliness lagged).
• Q2: C (SFBs were specifically lauded for perfect scores in accuracy and consistency).