What this demo is about
The same scoring engine behind the Session 3 Claude Skill, shown as readable Python instead of a chat response: aging math, a weighted 0-100 risk score, and rule-based segmentation.
Treasury Management • Colab • 30-45 min
AR Aging & Collections Prioritizer
Launch a Colab notebook that reproduces the AR Aging & Collections Prioritizer Claude Skill as a transparent, cell-by-cell walkthrough: aging buckets, a weighted risk score, four-segment collections strategy, and a ranked worklist.
AR aging & DSO estimation
Receivables segmentation
Collections worklist ranking
Why Colab helps here
Every chart and table in the notebook is produced by calling the exact same functions the Skill calls, so students can inspect, question, and re-run the logic instead of trusting an opaque result.
Run Modes and Expectations
Colab
Best for a classroom walkthrough with zero local setup and visible Python cells for each aging, segmentation, and ranking step.
What students do
Run the aging/DSO summary, compare the segmentation output to the "Receivables Strategy by Customer Type" slide, re-rank the worklist under a cash-crunch weighting, and reflect on data gaps.
Wait expectation
Lightweight by design. The optional scale-testing section at the end (50K-100K synthetic rows) takes a few extra seconds and is clearly separated from the core walkthrough.
How to use it
- Open the notebook in Colab and run the setup and data-loading cells.
- Compare the segmentation chart and table against the "Receivables Strategy by Customer Type" slide from Session 3.
- Review the risk-ranked collections worklist and the CFO "top N accounts" scenario.
- Re-run with a different weighting and discuss which accounts moved, and why.
This is a teaching notebook, not a production collections model. The thresholds are illustrative; the point is to make segment-specific response, not accuracy, the classroom discussion.