DATA-DRIVEN REVENUE RECOVERY: SQL-BASED VALIDATION FRAMEWORKS FOR BILLING ACCURACY

Vaishnavi Khati

doi:10.67809/ijetrm.1789830899

Abstract

Billing accuracy is often treated as a back-office housekeeping matter rather than a source of retrievable revenue.
This paper argues that treating it as the latter, and building the analytical capability to do so, is one of the more
reliable ways for finance and billing operations teams to improve margin without touching pricing or headcount.
Drawing on published industry benchmarks and a structured practitioner methodology, it sets out a SQL-first
approach to full-population billing validation, moving beyond the sampling-based reconciliation that most
operators still rely on. The paper introduces the RECOVER framework, a seven-stage methodology covering
reconciliation, discrepancy extraction, segmentation, root-cause analysis, stakeholder validation, control
embedding and reporting, designed to convert one-off recovery exercises into a repeatable capability. It closes
with an honest account of where the approach struggles, including data quality prerequisites, the limits of
correlation-based detection, and the organisational politics of reallocating analyst time to a function that rarely
has a budget of its own. The intended reader is a business analyst, finance manager or billing operations lead who
suspects their organisation is leaving money on the table but lacks a structured way to prove or fix it.

Keywords

Billing validation, revenue recovery, SQL data analysis, reconciliation, gap analysis, user acceptance testing (UAT)

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