AI Diligence and Loan Monitoring for a PE Fund

Key results

20-30 hours

of analyst time reclaimed per deal

~70%

less manual payment reconciliation

~$150K-$225K

in modeled annual labor value

AI Diligence Workflow

Summary

The client is a US mid-market private equity fund that runs both acquisition diligence and a private-credit book. Data-room files, loan schedules, and bank activity were turned into review questions by hand, and analysts spent most of each deal finding problems instead of weighing them.
We built two workflows. One runs a first-pass review of every data room and turns it into source-linked questions by risk area. The other matches expected loan payments to bank activity and routes exceptions to a reviewer. Analysts and qualified professionals make every judgment call.
Cooperation Period
Ongoing
Location
USA
Industry
Private Equity & Private Credit
Service Provided
AI Adoption
mortgage execs

Business Challenge

The deal team reviewed large data rooms on tight committee timelines, and the credit side tracked borrower payments by hand.

  • Messy data rooms. Documents were missing, outdated, duplicated, or inconsistent across financial statements, tax records, bank data, loan files, and management spreadsheets.
  • Late questions. Issues with revenue quality, EBITDA add-backs, related-party transactions, tax exposure, customer concentration, and debt obligations often surfaced after significant analyst time had already been spent.
  • Loan payments checked by hand. Payments were matched against schedules and bank statements line by line, so a short pay or a missing quarterly report could surface well after the fact.
  • Every flag had to be traceable. The fund needed one workflow with a clear audit trail from each flag back to its source.

What We Did

  • 1

    Acquisition Diligence Workflow

    For each new deal, the agent runs a first-pass review of the full data room:

    •Indexes every file, from audited statements to one-off management spreadsheets

    •Checks for missing documents, outdated versions, duplicates, and conflicting figures across sources

    •Extracts evidence on revenue, EBITDA, working capital, debt, and tax exposure

    •Links every extracted figure and flagged item to its source document and page

    •Organizes open diligence questions by risk area

  • 2

    Private-Credit Monitoring Workflow

    As loan files and bank data arrive, the system continuously:

    •Builds expected-payment records by borrower and due date

    •Matches them against actual deposits

    •Flags short or late pays, missing reports, and unusual transfers

    •Routes each exception to the right reviewer

    How it works in practice: a short quarterly payment is flagged with the loan terms and bank line attached and sent to the credit reviewer that week.

  • 3

    Scope and Controls

    •Raw files stay unchanged; agent outputs are versioned separately

    •Every extracted figure and flag links back to the underlying document

    •Assumptions and unresolved questions are labeled, not presented as findings

    •Final tax, legal, accounting, credit, and investment conclusions stay with qualified professionals

Project Results

What changed since launch:

Diligence starts from a complete picture
The first-pass output arrives with missing files, outdated versions, conflicting figures, and open questions already organized by risk area, each with a source link.
Issues surface while there’s still time to act
A short pay or a missing quarterly report now appears as a flagged exception within the same week the data arrives.
Analyst attention goes only to unresolved items
Clean matches pass through without review. What lands in the queue is the deposit with no matching schedule, the short payment, the unexpected fee, the missing report.
CPA and controller handoffs got cleaner
Principal, interest, fees, reimbursements, and related-party transfers arrive separated for professional review, supporting documents attached.