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.

Null and Missing Value Handling

Financial datasets often contain nulls in amounts, dates, or category fields. A strong answer should explain when to replace nulls with defaults using COALESCE, when to exclude rows, and when to flag them for review instead of silently filling values.

SELECT report_id, COALESCE(fee_amount, 0) AS fee_amount
FROM argus_financial_extract;

Duplicate Detection

Duplicate transactions or report rows can inflate totals and distort financial metrics. Candidates should describe how they identify duplicates using grouping logic or row-level checks on business keys such as account, posting date, and amount.

SELECT account_id, posting_date, amount, COUNT(*) AS duplicate_count
FROM argus_financial_extract
GROUP BY account_id, posting_date, amount
HAVING COUNT(*) > 1;

Standardizing Categorical and Text Values

Financial reports often rely on consistent labels for products, channels, or adjustment types. Cleaning includes trimming whitespace, normalizing case, and mapping inconsistent values like 'fee', 'Fee ', and 'FEES' into one reporting category.

SELECT TRIM(LOWER(revenue_type)) AS normalized_revenue_type
FROM argus_financial_extract;

Validating Numeric Fields

Amounts should be checked for invalid negatives, unexpected zeros, or out-of-range values before aggregation. The goal is not just to clean data, but to separate legitimate business cases from data-quality issues that need escalation.

SELECT report_id, amount,
       CASE
         WHEN amount IS NULL THEN 'missing'
         WHEN amount < 0 THEN 'review_negative'
         WHEN amount = 0 THEN 'review_zero'
         ELSE 'valid'
       END AS amount_status
FROM argus_financial_extract;

Auditability and Flagging

In financial reporting, it is important to preserve the original record and document any transformation. Good practice is to create cleaned fields and quality flags rather than overwriting source values, so downstream users can trace how the final report was produced.

SELECT report_id,
       amount AS original_amount,
       COALESCE(amount, 0) AS cleaned_amount,
       CASE WHEN amount IS NULL THEN 'imputed_zero' ELSE 'original' END AS cleaning_flag
FROM argus_financial_extract;