SAAS, MID-MARKET
An AR Aging Report That Shows What the Balance Actually Means.
DSO
tracked in real time
Credit memos
surfaced and quantified for the first time
2 audiences
served from one model
The Problem
The company was a mid-market SaaS business running on Intact. Their AR aging report was a standard export that mixed genuine receivables with unapplied credit memos, creating a balance that did not reflect what was actually owed.
Collections was chasing balances that were partially or fully offset by credits that had never been applied. Finance leadership could not get a clean DSO number because the aging data was polluted with process errors. The Controller knew the report was unreliable but had no way to separate the real receivables from the noise.
Two audiences needed this data: the Controller for financial reporting and the collections team for daily workflow. Both were getting the same flawed export.
The Outcome
Novexa built a Power BI AR aging model that separated genuine receivables from unapplied credit memos and process errors. For the first time, the Controller could see what was actually owed versus what was a credit application problem.
DSO was tracked in real time from clean data. Credit memos were surfaced and quantified so finance could address the application backlog systematically rather than discovering it during collections calls.
The model served both audiences from one source: the Controller got a clean aging summary for financial reporting, and the collections team got a prioritized list of genuine receivables to work. The core model is complete, with collections workflow integration ongoing.
The Approach
- 1.
Analyzed the raw AR data
Extracted the full AR ledger from Intact and categorized every open item: genuine invoices, unapplied credits, partially applied payments, and duplicate entries. Quantified the total credit memo backlog for the first time.
- 2.
Designed a segmented aging model
Built a Power BI data model that separated receivables into clean categories: billed and due, credit offsets pending application, and disputed or escalated items. Each category had its own aging logic so DSO calculations were based on genuine receivables only.
- 3.
Built dual-audience views
Created two dashboard views from the same model. The Controller view showed aging summaries, DSO trends, and credit memo exposure. The collections view showed prioritized customer balances with aging buckets and contact history.
- 4.
Documented the credit memo workflow gap
Delivered a written analysis of why credit memos were accumulating unapplied, including process gaps in the order-to-cash cycle. This gave finance the evidence needed to address the root cause rather than just cleaning up the symptoms in reporting.
An AR aging report that mixes clean receivables with application errors is not a report. It is a distraction. The right model shows you what is actually owed, what is a process problem, and where to focus the collections conversation.
Identifying details have been generalized to protect client privacy. Outcomes reflect actual engagement results.
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