AR Credit Risk & DSO Analysis Copy link

🚨 Blitz data = Excel worksheet UDFs, not MCP tools/connectors. Extract data only by writing =br_...() into a cell (via execute_office_js) and reading the spill — never tool_search for br_*, never refresh_mcp_connectors, and never conclude "no Oracle EBS / Blitz connector is set up" (the Blitz Report add-in is an Excel add-in, not an MCP connector, so it never appears in the connector list — its absence there is expected). Load the extraction mechanism first: read_skill("br-run-reports").

Performance: read br-analytics-standards at the start of every run. The payment-behaviour scoring reuses the technique in br-rfm-analysis — read it when that add-on runs.

When to use

"Which customers are a credit risk", "review credit limits", "who is close to / over their limit", "why is DSO up this quarter", "credit-review worklist for the committee". For chase-this-week collections prioritization use br-ar-collections-worklist (it owns dunning); this skill is the slower-cadence credit and DSO view. They compose: run this first, hand the high-risk names to the worklist.

Inputs

• Operating Unit (required) — ask if not supplied.
• As-of date — default today; say so.
• Optional: customer scope, the payment-behaviour add-on (extra extract — offer once, yes/no).

Reports used (authoritative names — do NOT broad-search for them)

Pass the exact names; fall back to a search only on #BLITZ_REPORT_NOT_FOUND.

Workflow

1. Extract snapshot, limits, aging and lookback for the OU (one call each). Run AR Applied Receipts only if the behaviour add-on was accepted.
2. Score each customer on four dimensions, each 0–25, with the raw driver kept beside the score:
   • Utilization — open balance vs credit limit (over-limit caps the dimension at 25). Customers with NO limit set are their own class — "unlimited exposure", listed separately, never scored as safe.
   • Aging severity — share of balance past 60/90 days.
   • Trend — balance growth over the lookback periods (growing balance + worsening aging is the compound signal to call out).
   • Payment behaviour — weighted days-to-pay vs terms from applied receipts; when the add-on was skipped, this dimension reads "not scored" and the total is out of 75 — SAY SO, never silently rescale.
3. Grade A–D from the total (thresholds are CONFIG placeholders — label them), and produce the credit-review worklist: grade C/D + over-limit + unlimited-exposure customers, ranked by open balance, one plain-language line each ("82% utilized, balance +40% in 3 months, pays 18 days late").
4. DSO decomposition: DSO from aging balance vs credited sales proxy (lookback periods), split by customer segment/profile class — which segment moves the number, and whether the driver is balance growth or payment slowdown. Trend across the lookback.
5. Dashboard (br-data-dashboard): grade distribution, exposure by grade, DSO trend, top-10 exposure list.

Hard rules

• A recommendation is a REVIEW candidate, never "cut this customer's credit" — credit decisions are the customer's policy, and the sheet says so.
• Never net credit balances into risk scores silently — a large unapplied credit is its own callout, not a risk reducer.

Degradation rules

• No AR Customer Credit Limits rows (site doesn't use limits) → utilization dimension reads "limits not used", scoring proceeds on the other dimensions with the reduced denominator stated.
• Thin lookback history → trend dimension "not scored", stated.

Talk in the user's language

Customer names, balances, grades — never br_* functions or helper sheets.