Context

Financial analysts at Argus Information & Advisory Services often work with transaction and reporting extracts that contain missing values, duplicates, inconsistent labels, and invalid amounts. Clean inputs are critical because even simple aggregation queries can produce misleading financial results if the source data is not standardized first.

Core question

Explain the common data-cleaning techniques you use when preparing financial reports in SQL. Your answer should cover:

1. How you identify and handle missing or null values
2. How you detect duplicates and inconsistent categorical values
3. How you validate numeric fields such as revenue, fees, or balances
4. How you standardize dates and text fields before aggregation
5. How you preserve auditability so business users can trace adjustments

Scope guidance

Keep the discussion practical and SQL-focused. The interviewer is looking for a structured explanation of the checks you would perform before building summaries, month-end reporting tables, or analyst-facing outputs in Argus reporting workflows.