The RBI bank data quality index for scheduled commercial banks rose to 93.3 in June 2026 from 90.7 in March, led by a sharp improvement in accuracy. The aggregate score is rated “good,” but consistency edged down and remained the weakest of the four reporting dimensions.
RBI bank data quality index: what improved
The Reserve Bank of India released its June-quarter Supervisory Data Quality Index on September 22. The measure evaluates the accuracy, timeliness, completeness and consistency of regulatory returns and microdata used in supervisory assessments.
Accuracy supplied most of the improvement, rising 9.5 points to 96.3. Completeness moved to 96.9 from 96.4, while timeliness rose to 92.8 from 92.1. Consistency was the exception, easing one-tenth of a point to 87.3.
Why the aggregate score matters
Supervisors rely on bank submissions to assess capital, liquidity, asset quality, earnings and large exposures. Better data does not automatically mean lower credit risk, but it improves the evidence available to identify that risk. Errors or late submissions can obscure changes in a bank’s balance sheet precisely when supervisors need a current view.
RBI classifies a score above 90 as good, 80 to 90 as acceptable, 70 to 80 as needing improvement and below 70 as a major concern. Only one institution scored below 80 in June, according to the release.
How bank groups compared
Small finance banks recorded the highest group score at 93.8, ahead of public-sector and foreign banks at 93.7 each. Private-sector banks improved to 92.2 from 89.3. These are aggregate group readings, so they should not be used to infer the score of an individual bank.
The index covers 87 scheduled commercial banks. It tests adherence to the reporting principles in RBI’s 2026 supervisory-return directions for commercial banks and small finance banks, connecting the score to a defined governance framework rather than a general survey.
What investors should not infer
A higher sDQI is not an asset-quality rating, stress-test result or guarantee that reported loans will perform. It says that the information submitted for supervision met stronger quality standards. Credit losses, liquidity and capital still require their own measures.
Equally, the small decline in consistency deserves attention because stable definitions and reconciled returns make trends comparable. If accuracy rises while the same data is not consistent across submissions, supervisors may still need remediation and explanation.
What changes next
Banks should preserve the accuracy gain while improving cross-return consistency. Boards and audit committees can use the components to locate weaknesses in data lineage, validation and filing controls. Future quarterly releases will show whether the jump is durable.
Lapaas Voice’s NaBFID fundraising analysis explains why reliable institutional reporting matters to capital decisions, while the RBI InvIT and REIT valuation update shows another way the central bank is tightening how regulated institutions measure and report financial exposures.
Bottom line
The RBI bank data quality index improved convincingly to 93.3, with accuracy doing most of the work. The useful reading is not that bank risk disappeared, but that the supervisory dataset became more reliable. Consistency is now the clearest aggregate weakness to monitor.
Verified facts
| Measure | June 2026 |
|---|---|
| Overall sDQI | 93.3 |
| Accuracy | 96.3 |
| Completeness | 96.9 |
| Timeliness | 92.8 |
| Consistency | 87.3 |
| Banks covered | 87 |
Frequently asked questions
What does sDQI measure?
It measures the accuracy, timeliness, completeness and consistency of supervisory returns and microdata.
Does 93.3 mean banks are financially safe?
No. It is a data-quality score, not an asset-quality or solvency rating.
Which component was weakest?
Consistency, at 87.3, was the lowest aggregate component in June.
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