Ensuring Regulatory Data Accuracy

Context

Regulatory reporting requires complete, accurate, and auditable data. Interviewers ask this question to assess whether you can use SQL and data controls to prevent incorrect submissions.

Question

Explain how you ensure the accuracy of data submitted to regulatory bodies. Your answer should cover:

1. How you validate source data before reporting
2. How you use SQL checks such as filtering, aggregation, and reconciliation
3. How you detect missing, duplicate, or inconsistent records
4. How you create an audit trail and handle corrections

Scope Guidance

Keep the answer practical and structured. The interviewer is not looking for advanced theory; they want a clear process for using SQL and data quality controls to verify reporting datasets before submission, plus how you would document and monitor the process over time.

Source Data Validation

Before building a regulatory report, validate that required fields are present, data types are correct, and values fall within expected ranges. This prevents bad records from entering the reporting layer and reduces downstream reconciliation issues.

SELECT *
FROM regulatory_transactions
WHERE report_date IS NULL
   OR amount IS NULL
   OR amount < 0;

Reconciliation with Aggregations

A common control is reconciling report totals against source-system totals using counts, sums, and grouped comparisons. If totals do not match at the right grain, the submission should be investigated before release.

SELECT reporting_period, COUNT(*) AS record_count, SUM(amount) AS total_amount
FROM regulatory_transactions
GROUP BY reporting_period;

Duplicate and Missing Record Checks

Regulatory submissions often require one record per business event. SQL can identify duplicate keys, unexpected nulls, or missing categories that indicate incomplete extraction or transformation logic.

SELECT transaction_id, COUNT(*) AS duplicate_count
FROM regulatory_transactions
GROUP BY transaction_id
HAVING COUNT(*) > 1;

Auditability and Repeatability

Accuracy is not only about correct numbers; it is also about being able to explain how those numbers were produced. A strong process stores query logic, run timestamps, source versions, and exception records so the report can be reproduced and reviewed.

Exception Handling

Not every issue should be silently fixed in-place. Good reporting processes isolate failed records, document the reason, and require review or remediation before those records are included in a final submission.

SELECT *
FROM regulatory_transactions
WHERE customer_id IS NULL
   OR status NOT IN ('SUBMITTED', 'APPROVED', 'REJECTED');