# AR Aging Analyzer & Collections Prioritizer: Free DSO & Collections Priority List Tool | Enso

> Paste your invoice ledger and instantly see aging buckets, DSO, a multi-factor customer risk score, and a collections priority list. Runs entirely in your browser, and your AR data never leaves your device.


Most collections teams start their day with an AR aging export and a mental sort: which customers are actually a risk, which invoices just crossed a threshold, and who needs a call today versus a gentle nudge next week. That triage is manual, repetitive, and easy to get wrong when the list runs into hundreds of rows.

Paste your invoice ledger above (or click "Load sample data" to see it work first) and this tool does the triage for you: aging buckets, DSO, a multiplicative risk score per customer built from their payment history, current aging, and outstanding severity, and a collections priority list sorted by urgency with a suggested next action for each invoice.

## How the risk score is calculated

The risk score looks at three things about a customer, not just the balance sitting in front of you:

- **Payment history.** Have they historically paid on time, or do invoices routinely drag past terms? This comes from the customer's full paid-invoice history, not just what's currently open.
- **Current aging.** How old is their oldest open invoice right now? A customer who's usually reliable but has one invoice slipping past 60 days is a different risk than one whose oldest invoice was due yesterday.
- **Outstanding severity.** How large is their total open balance relative to what they normally bill in a month? A big number in isolation doesn't mean much until you know whether it's typical for that customer or wildly out of pattern.

Rather than averaging these three factors, the score multiplies them. That's deliberate: a single weak factor shouldn't sink a customer's score, but a customer who is bad on more than one axis at once, say a spotty payment history *and* a large, aging balance, is a materially different risk than one who's only slightly off on one dimension. Multiplying lets the score escalate fast when problems stack, while staying low for customers who are clean on all three fronts. The result is bucketed into Low, Medium, and High so you can scan the concentration table at a glance instead of comparing raw numbers.

Because payment history and typical billing volume both draw on a customer's full invoice history, the more history your CSV includes (not just currently open invoices), the more accurate the score.

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This is a simplified version of the collections engine inside Enso, which also automates the outreach itself with AI-tuned reminder emails, aging-based escalation, and payment reconciliation against your bank feed. If your team is still triaging this list by hand every week, [see how Enso automates collections](/features/collections/).