Product ยท M&A Due Diligence

Diligence a target's loan book with the same engine.

Point Klerum at an acquisition target's loan tape and it re-grades the whole portfolio, marks the incremental credit reserve, and drafts the diligence takeaways a buyer needs. It reuses the same workpaper, document, and exception tools as a loan review, and the model only ever sees anonymized figures.

Portfolio considerations

The diligence memo, drafted from the tape.

Import the target's loan tape and Klerum's AI drafts the takeaways an acquirer works from — concentration, credit quality, maturity walls, sector exposure, vintage, and data gaps — each grounded in the aggregated figures. It is a starting point to sharpen, not a decision, and no borrower names reach the model.

AI portfolio considerations for an M&A target
$1.24B across 641 loans and 613 relationships — the AI surfaces the maturity wall, the sector concentration, and the unclassified-sector data gap.
AI risk-rating re-grade

Are the target's ratings defensible?

The AI independently reassesses every relationship's assigned rating from its anonymized credit attributes, calling downgrades, affirmations, and upgrades with a confidence level and a written rationale. It runs over the full book, and the downgrade view feeds the credit mark.

  • A suggested grade and a downgrade / affirm / upgrade call on every relationship
  • A plain-language rationale for each call, and a confidence level
  • Portfolio totals: downgrades, downgraded balance, and net notches
  • The model sees only anonymized credit attributes, never borrower names
AI re-grade of a target loan portfolio
164 of 613 relationships downgraded, each with a suggested grade, confidence, and rationale.
Credit mark rolled up from the AI re-grade
Target reserve vs. acquirer reserve, with the incremental credit mark and editable expected-loss assumptions.
Credit mark

Turn the re-grade into a number.

The credit mark rolls the AI re-grade into dollars: each relationship's outstanding is multiplied by an expected-loss rate for its grade. The target's view uses its assigned grades, the acquirer's view uses the re-graded ones, and the difference is the incremental mark. Edit the loss assumptions to fit the deal and it recomputes.

Same posture, new use

Audit-grade diligence, humans deciding.

M&A diligence reuses everything that makes a Klerum loan review defensible: deterministic math, redacted inputs, and a human on every call. The AI re-grades, marks, and drafts; your team decides what the book is worth. Bring a target tape to a demo and watch it run.

Diligence a target on real numbers.

Bring an acquisition target's loan tape to a 30-minute demo and watch Klerum re-grade the book, mark the reserve, and draft the portfolio takeaways.