Data-Driven Revenue Recovery: SQL-Based Validation Frameworks for Billing Accuracy
Year: 2026
Type: preprint
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.
Author: Khati, Vaishnavi
Language: English
Subfield: Management Information Systems
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7212299596
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