Treasury Management • Intermediate • 30-45 min

AR Aging & Collections Prioritizer

Colab-based treasury notebook reproducing the AR Aging & Collections Prioritizer Claude Skill's aging, risk-scoring, and segmentation logic.

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What this demo is about

Deterministic, inspectable receivables intelligence

This notebook makes the AR Aging & Collections Prioritizer Skill's logic visible, cell by cell: aging buckets and a DSO estimate, a weighted risk score built from days past due, amount, dispute status, payment history, and revenue contribution, and a four-segment collections strategy — all readable Python, not a hidden model.

Learning objectives

Explain AR aging buckets and a DSO estimate as deterministic arithmetic, not a black-box model.
Compare a rule-based four-segment collections framework against a course slide framework.
Interpret a risk-weighted collections worklist and a CFO-facing cash-impact number.
Evaluate how changing risk weights reorders collections priority, and why that matters.

Run mode

Supported mode: Colab
Demo type: notebook launch guide
Primary launch surface: index.html

Teacher cue

Ask which accounts moved when the risk weights changed, and whether that reordering reflects genuine collections urgency or just a larger invoice amount.

Why this matters

Segment-specific response, not a single generic dunning process, moves cash

Business context

A treasury team needs to know not just who owes money, but which accounts need commercial escalation, which need tighter terms, and which are safe to automate — a single aging report doesn't answer that.

Key features

●Aging buckets and DSO estimate

●Weighted 0-100 risk score

●Four-segment collections strategy

●Risk-weighted worklist ranking

What students should note

The segmentation thresholds are illustrative, not universal. The exercise is to argue about where the cutoffs should sit for a real business, not to treat the numbers as ground truth.