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How a finance team cut overdue receivables by 23% in a month

AI
Ananya IyerJul 2, 2026 · 4 min read

One prompt, three workflows and a single AI agent turned late-payment chasing from a manual chore into an automatic, predictive process.

The problem

The team knew which customers tended to pay late — but only after the fact. Chasing was reactive, inconsistent and eating hours every week. They described the problem to Zinie in a single sentence: show me who's likely to pay late, and nudge them for me.

Within a few hours they had a working system: two apps, three workflows and one agent scoring every open invoice by likelihood of delay, then triggering the right reminder on the right channel at the right time.

The outcome

In the first month, overdue receivables dropped 23%. More importantly, collections happened roughly two weeks earlier, smoothing cashflow without adding a single headcount.

"It's like having a chief-of-staff for collections," the finance director told us. "We stopped reacting and started getting ahead of it."

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