How a global industrial services company reduced days sales outstanding and caught invoice errors before they became disputes.
This client processed millions of B2B invoices annually across multiple regions and service lines. Days sales outstanding was persistently elevated — driven by a combination of payment delays, disputed invoices, and an accounts receivable team that had no systematic way to prioritise their outreach across thousands of accounts.
The most costly problem was also the most preventable: a significant proportion of disputed invoices contained conformity errors — mismatches between invoice content and contracted terms — that were entirely avoidable if caught before dispatch. Instead, they reached customers, triggered disputes, stalled payment cycles, and generated expensive resolution work downstream.
AR teams were working from spreadsheets, using experience and instinct to decide which accounts to chase. High-value accounts with a history of prompt payment received the same attention as genuinely at-risk accounts. The process was manual, inconsistent, and impossible to scale.
Invoice errors were reaching customers intact. Payment risk was invisible until it became a problem. AR teams had no intelligent prioritisation — just a list sorted by due date.
Every additional day of DSO represented real working capital tied up unnecessarily. Every preventable dispute represented a relationship friction and a resolution cost the business had to absorb.
We trained ML models on the client's invoice and payment history — scoring each customer and invoice on payment delay and dispute probability using transaction patterns, payment behaviour, contract terms, and account-level behavioural signals.
We designed automated conformity checks that validated each invoice against contracted terms before dispatch — catching pricing mismatches, scope discrepancies, and formatting errors that had historically triggered disputes after the invoice had already reached the customer.
Microsoft Power Platform dashboards gave AR teams a risk-ordered view of their portfolio — with payment risk scores, past-communication summaries, account context, and guided outreach scripts replacing the manual spreadsheet process entirely.
Actual payment outcomes were fed back into the model continuously — improving prediction accuracy as the model learned from real outcomes and adapted to changes in customer behaviour and billing practices over time.
The pre-dispatch conformity checks delivered impact faster than the payment risk models — because preventing a dispute costs a fraction of resolving one. The AR team's reaction to the risk-ordered dashboards was equally swift: within weeks, outreach patterns had changed and the first measurable DSO improvements were visible.
Analysis revealed that a disproportionate share of disputes traced back to a small number of recurring error types in invoice content — patterns the AR team had long suspected but never been able to quantify or systematically prevent.
Moving from a due-date list to a risk-ordered dashboard changed not just what the team worked on, but how they worked. Guided outreach scripts improved conversation quality and reduced the number of contacts needed per resolved account.
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