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Price a secondary loan tape

A secondaries buyer receives a 150-loan tape from an LP selling a 2019-vintage direct lending fund interest.

Credit secondariesPublic + sample docsBuildable in-house
INPUTSecondary loan tape (100+ loans)AI STEPSNormalize the tape · Match to publicmarks · Flag rich marksOUTPUTBenchmarked tape with marks flagged richor cheapHUMAN CHECKThe bid

Today vs automated

TODAY
  1. Clean the tapeAssociate · manual
  2. Look up each borrowerAssociate · manual
  3. Price buckets by handVP / counsel · manual
  4. Email / meeting hand-offAssociate → VP / counsel
AUTOMATED
  1. A seller’s loan tape arrivesTrigger: starts on its own
  2. Normalize the tapeAutomated · Document parser
  3. Match to public marksAutomated · LLM
  4. Flag rich marksAI draft, human review · Model template
  5. The bidApprove / override · VP / counsel

AutomatedAI draft, human reviewHuman onlyTools: Data room, Document parser, LLM, Model template

Estimated valueestimate

120–1,080 analyst hours a year
$18k–$270k of time, vs an assumed $10k–$40k tool cost

Net: −$22k to $260k a year net of tool cost; at the low end of the assumptions it does not cover the tool cost.

Manual20–40 h per tapeWith AI4–8 h per tape, review included
Assumptions
  • 20–40 analyst hours per tape today; 4–8 hours with an agent, including review
  • 10–30 tapes a year for one team
  • $150–250 per hour loaded cost
  • Tool or build cost $10,000–40,000 a year (in-house)

Estimate, based on the assumptions shown; your numbers will differ. How we estimate

What else it can do for the team

  • Group loans by sector, size and lien for bucket pricing
  • Pull public BDC marks on shared names
  • Flag loans with recent amendments or PIK
  • Draft the bid rationale memo
  • Track tapes seen and priced in the deal log
How you get thereFour steps from prompting to an agentic workflow; most teams start at step 4.
1 · PromptingAn analyst asks a chat tool about one tape.
Needs: an approved AI tool, a confidentiality policy.
Risk: unchecked answers, data pasted outside policy.
2 · WorkflowA fixed pipeline extracts the same fields into your template every time.
Needs: field definitions, a template, document parsing.
Risk: breaks silently when formats change.
3 · AgentIt watches the inbox or data room, runs the checks and drafts the output.
Needs: inbox/doc-store access, system write-back, logging.
Risk: acting on a misread; needs review gates.
The automated lane above
4 · Agentic workflowSeveral agents work across the deal or book; people approve exceptions.
Needs: shared data model, permissions, audit trail, exception queue.
Risk: errors compound across steps; needs monitoring.
Typical starting point
Solution optionsBuy (7 vendors listed), build or hybrid, with the trade-offs of each.
Buy
Octus, The LoanEdge, Chronograph, Cobalt, 73 Strings, Oxane Partners, WhaleBlue
Fastest start; you adapt to the product's data model and pricing.
Build
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.
Hybrid
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 team20–40 h → 4–8 h per tape (estimate); coverage goes from the sections someone had time to read to every clause and schedule checked against the term sheet.
ROLESAssociates move from reading and retyping to checking cited exceptions.
CYCLE TIME20–40 h → 4–8 h per tape (estimate)
COVERAGEFrom the sections someone had time to read to every clause and schedule checked against the term sheet.
CONTROLSEvery output cites its source; approvals and overrides are logged.

Which other BDCs lend to this borrower? →

How it works

Full title: Credit secondaries: price a 150-loan tape in an afternoon, with every mark benchmarked to public BDC marks

“The seller marks it at 98. Three BDCs mark the same loan at 84–90. Here are the 20 loans that set the price.”

Scenario

SAMPLE: a secondaries buyer receives a 150-loan tape from an LP selling a 2019-vintage direct lending fund interest. Bids are due Friday.

What the human still decides

The bid. The agent benchmarks and buckets; the team decides the discount and whether the public comps are relevant.

Why it matters

GP-led credit secondaries are scaling: Bridgepoint Credit moved €1.2bn into a Pantheon-led continuation vehicle and Partners Group is exploring a ~$800m one (Sep 2026). Public BDC marks are free comps most tapes are never checked against.

What the record shows

Checked 2026-10-15.

Recent credit CV sellers are moving seasoned loans (Bridgepoint's 2017-vintage €1.2bn; Partners Group's 2018/2020 funds). Where a tape loan's borrower also sits in a public lender's book, that lender's reported mark is an outside cross-check on the seller's mark; the BDC section shows how often the same credit is marked differently by different holders (see /sectors/bdcs/).

Try it: prompt and data
For each loan on this SAMPLE tape, find matching borrowers in the June 30, 2026 BDC SOI data and list each holder's FV/cost with a link. Flag loans where the tape mark exceeds the max public mark by more than 3 points. Bucket the tape by those flags. Do not recommend a price.

Data

Benchmarks come from public SEC filings. Real tapes are confidential: SAMPLE data only here.

Who uses it, vendors and build
Segments
Credit secondaries
Who uses it
Secondaries associate, Pricing / valuation, LP portfolio manager
Build
Buildable in-house
In-house
Reuses this site's who-holds matching. Estimate: 1–2 weeks; fuzzy name matching needs human review.
Last verified
2026-10-15

Vendors that claim this capability

Tags reflect each vendor's own public claims at the linked page (checked 2026-10-15). Not tested, ranked or endorsed.

Sources and related
In the news.

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Not investment advice. Names marked SAMPLE are fictional. Found an error? See the corrections log.