Businesses often manage invoices and payments across different systems, making it difficult to identify discrepancies, missing payments, duplicate records, and inconsistent financial data.

This case study demonstrates a structured approach to identifying and investigating these issues.

Reviewed invoice and payment datasets for missing values, inconsistent formats, duplicate records, and potential data quality issues.

Used SQL-based validation and reconciliation logic to compare invoice and payment records, identify unmatched transactions, and investigate discrepancies.

Organized findings in Excel to make discrepancies easier to review, classify, and investigate.

Documented identified issues and proposed practical validation controls to improve data reliability and reduce manual investigation.

Invoice and payment reconciliation analysis

SQL validation and matching logic

Excel-based discrepancy tracking

Data quality findings and exception reporting

Recommendations for improving reconciliation controls

This approach demonstrates how structured data analysis can help businesses identify financial data inconsistencies, improve reporting confidence, and make reconciliation processes more transparent.

The findings are based on demonstration data; no live client implementation or measured financial results are claimed.