Monitor portfolio · PCT-076 · Sourced card
Early-warning watchlist draft
A head of portfolio management wants a monthly candidate watchlist that pulls together the soft signals that usually show up before a covenant breach.
Worked example: Six soft signals in two quarters and no covenant breach: recomputed leverage up from 4.71x to 5.29x, DSO up 9 days, a late certificate and a request for wider add-backs. The name is a candidate for watchlist review. See the walk-through ↓
Today vs automated
- Read packs and emailsAnalyst · manual
- Search news by nameAnalyst · manual
- Write the memoPM · manual
- Email / meeting hand-offAnalyst → PM
- Month-end: new packs and filings are inTrigger: starts on its own
- Collect soft signalsAutomated · Document store
- Score against your rulesAutomated · LLM
- Draft a cited briefAI draft, human review · Monitoring model
- Who goes on the watchlist, the plan for each name, and when to call the sponsorApprove / override · PM
AutomatedAI draft, human reviewHuman onlyTools: Inbox / portal, Document store, LLM, Monitoring model, Loan system
Estimated valueestimate
110–320 analyst hours a year
$16k–$81k of time, vs an assumed $20k–$100k tool cost
Net: −$84k to $61k a year net of tool cost; at the low end of the assumptions it does not cover the tool cost.
Assumptions
- 15–30 analyst hours per monthly book review today; 3–6 hours with an agent, including review
- 12–12 monthly book reviews a year for one team
- $150–250 per hour loaded cost
- Tool or build cost $20,000–100,000 a year (both)
Estimate, based on the assumptions shown; your numbers will differ. How we estimate
What else it can do for the team
- Pre-fill the watchlist committee pack
- Track signal history per name
- Tune rules after each review
- Alert PMs between monthly reviews
- Feed results back into underwriting lookbacks
Worked walk-throughKey catch: Six soft signals in two quarters and no covenant breach: recomputed leverage up from 4.71x to 5.29x, DSO up 9 days, a late certificate and a request for wider add-backs. The name is a candidate for watchlist review.
1. The input
SAMPLE: fictional company, illustrative figures. Not a real borrower, deal or lender group.
Everything the team already holds on Project Meridian: monthly packs, the Q2 certificate tie-out, the delivery log and the sponsor’s request. SAMPLE pack →
2. What the AI produces
| Signal | What changed | Source |
|---|---|---|
| Leverage trend | Recomputed leverage 4.71x → 4.95x → 5.29x over three quarters | Monitoring history |
| Working capital | DSO 48 → 57 days | Monthly packs |
| Operations | Visits per clinic per day 31.2 → 29.4 | Monthly packs |
| Reporting | Q2 certificate delivered 9 days after the deadline; reported EBITDA does not tie to monthly packs | Delivery log; Q2 tie-out |
| Request | Sponsor asks for wider add-back caps and a 30-day Q3 reporting delay | Amendment request, Oct 2026 |
| Sector item | Regional payer contract in transition (borrower’s own disclosure in the request) | Sponsor letter |
Draft brief (excerpt): “Six soft signals in two quarters; no covenant breach on certified or recomputed figures. Recomputed headroom has narrowed. Candidate for watchlist review. Suggested next steps for the committee: request the sponsor’s Q4 forecast and payer-contract status before responding to the amendment request.”
3. What a person still checks
- Decide whether the name goes on the watchlist and what the plan is.
- Weigh signals the documents don’t show: sponsor support, relationship history, lender-group views.
- Tune the rulebook: which signals count and how many trigger a brief.
4. Where it breaks
- Signal rules tuned on one sector over-flag another (DSO moves differ by business model).
- News matching on company names produces false positives; private borrowers often have little coverage.
- An early-warning list is a prompt for review, not a forecast.
How you get thereFour steps from prompting to an agentic workflow; most teams start at step 4.
Needs: an approved AI tool, a confidentiality policy.
Risk: unchecked answers, data pasted outside policy.
Needs: field definitions, a template, document parsing.
Risk: breaks silently when formats change.
Needs: inbox/doc-store access, system write-back, logging.
Risk: acting on a misread; needs review gates.
The automated lane above
Needs: shared data model, permissions, audit trail, exception queue.
Risk: errors compound across steps; needs monitoring.
Typical starting point
Solution optionsBuy (24 vendors listed), build or hybrid, with the trade-offs of each.
Moody's, Allvue, Hebbia, Orion (Private Credit AI), Lev, 9fin, Debtwire, The LoanEdge, Chronograph, Cobalt, 73 Strings, Indus Valley Partners (IVP for Credit), Blueflame AI, CovenantIQ, CredCore, Oxane Partners, WhaleBlue, Henon, Acuity Analytics, Standd, CarbonAI, Ellis, Goldslate, Raven (RavenRisk AI)
Fastest start; you adapt to the product's data model and pricing.
An LLM API, a document parser, a vector or document store, your data warehouse, and a review queue.
Fits your definitions exactly; you own maintenance and controls.
Vendor extraction feeding your own checks, templates and systems.
Common middle path; two things to integrate and govern.
Listing is not endorsement.
What changes for the team15–30 h → 3–6 h per monthly book review (estimate); coverage goes from a sample of borrowers, quarterly to every borrower, every delivery.
How it works
Full title: Early-warning brief across the book: financials, reporting behaviour, requests and news in one cited watchlist draft
“No breach yet. Five signals in one quarter.”
Scenario
A head of portfolio management wants a monthly candidate watchlist that pulls together the soft signals that usually show up before a covenant breach.
What the human still decides
Who goes on the watchlist, the plan for each name, and when to call the sponsor. AI drafts candidates; the committee decides.
Why it matters
Large data providers launched early-warning models in 2026 built on soft events, and the Boston Fed points to PIK and deferrals as signals; teams want those signals gathered per name.
What the record shows
Checked 2026-10-18.
Moody's Analytics and Allvue launched a private credit model in October 2026 that estimates soft events such as waivers and PIK before missed payments; FINBOURNE and Alkymi (Feb 2026) market early detection from borrower documents and covenant monitoring.
Try it: prompt and data
For each borrower, review the attached packs, delivery log, request log and news items. List each soft signal with its date and source. Apply our rules (attached) and draft a half-page brief for any borrower with three or more signals. Do not predict default or assign ratings.
Data
Walk-through uses a SAMPLE deal pack: fictional company, illustrative figures. In practice the inputs are your own confidential borrower or fund documents; keep them inside an approved environment.
Who uses it, vendors and build
- Segments
- Direct lending, ABF / asset-based
- Who uses it
- Portfolio manager, Head of portfolio management, Risk
- Build
- Vendor + in-house
- In-house
- Needs a signal rulebook owned by the credit team; vendors supply models and feeds.
- Last verified
- 2026-10-15
Vendors that claim this capability
- Allvue: Data partner on the EDF-X Private Credit Model, portfolio-monitoring
- Moody's: EDF-X Private Credit Model: early signs of borrower stress
- Standd: portfolio-monitoring, credit-stress-tracking
- 9fin: situation-tracking
- Debtwire: situation-tracking
- 19 more vendors claim related capabilities. See all vendors for this stage →
Tags reflect each vendor's own public claims at the linked page (checked 2026-10-15). Not tested, ranked or endorsed.
Sources and related
- Moody's Analytics and Allvue: EDF-X Private Credit Model flags soft stress such as covenant waivers and PIK (1 Oct 2026)
- FINBOURNE and Alkymi: integrated private credit risk monitoring (24 Feb 2026)
- Federal Reserve Bank of Boston: early warning signals in private credit (2026)
- Deal packs used in the walk-throughs (SAMPLE, fictional companies)
Related cards
- PCT-071 Every borrower's quarterly pack, spread into one monitoring model: covenant headroom tracked quarter by quarter
- PCT-074 Amendment or waiver request in: what is being asked, who has to consent, and what it does to the covenant
- PCT-011 Cash to PIK, caught in the filing: flag every loan that switched to paying in kind this quarter
Next step
Related use cases
Get new walk-throughs
Not investment advice. Names marked SAMPLE are fictional. Found an error? See the corrections log.