Fund AI use cases
Every fund conference now has an AI panel, yet few of them say what anyone has actually deployed. This library answers that one job at a time: what the AI does, how strong the evidence is, what it can't do, and what you would otherwise do instead.
Use-case library
One page per job: what the AI actually does, how honest the evidence is, what it can't do — and what you would otherwise do instead.
Fund operations17Deal sourcing3Portfolio management4Every assessment carries the date it was verified — read it as true at that date, not as a standing promise.
Use-case library
Each page covers one job across fund operations, deal sourcing and portfolio management, and is built the same way:
- The task: the pain, in the words of the people who do the job.
- The evidence: graded on every page as production-verified, vendor-claimed or our own assessment.
- The limit: what the AI can't do, and where the human sign-off sits.
- The alternatives: usually your administrator, a law firm, a specialist platform or another analyst, not a robot.
- What it takes: the data you need first, the effort, an order-of-magnitude cost, and the volume below which it isn't worth doing.
Two notes before you read:
- Dates: AI changes faster than any library. Each page carries the date it was verified, so read it as true at that date.
- Choice: in a regulated group the tool is often chosen centrally. The pages then tell you what to ask of the tool you are given.
AI in fund operations
- Capital-call & distribution notice processing
Notices in any format become cash-flow data — but a human still owns the wire.
- Investor onboarding & AML/KYC chasing
Classify documents, chase gaps, triage screening hits — the AML sign-off stays a named person's call.
- Annex IV & regulatory report assembly
Mostly a deterministic rules job, not AI — the honest exception to the hype.
- NAV break & reconciliation triage
Cluster, rank and hypothesise on exceptions — but a model can't clear its own breaks.
- Side-letter obligation extraction
Prose promises become a deadline register — a genuine unstructured-to-structured AI job.
- DDQ & RFP answering
Draft from your approved answer bank — but a human signs off every regulated answer.
- Board packs & minutes first drafts
Summarise the pack, draft the minutes — the board still adopts the record.
- Fee & carry verification vs the LPA
AI reads the terms; a deterministic model computes the number. Emerging, honestly flagged.
- Investor-services inbox triage
Classify, route and draft TA queries — but nothing that moves money acts on its own.
- Closing-set consistency review
Cross-read PPM vs LPA vs subs for mismatches — the legal call stays human.
- Fund financial statements tie-out
Tie every figure in the draft accounts to the trial balance and holdings; the board approves, the auditor opines.
- Fund expense allocation review
Test each invoice against the LPA expense clauses and recalculate the split; a person decides what the fund bears.
- Year-end audit PBC support
Match the auditor's request list to what was supplied and tie it to the ledger; management approves every upload.
- CRS & FATCA investor classification
Read self-certifications, test for indicia, draft the reportable list; a person makes the classification.
- Quarterly investor report drafting
Draft the LP letter and trace every figure to the capital accounts; the IR lead edits and releases it.
- Regulatory change mapping
Read a new rule into obligations and map each to policies and controls; compliance rates the gaps.
- Delegate & service-provider oversight
Read SOC 1 reports and KPI packs and map exceptions to your controls; the oversight conclusion stays yours.
AI in deal sourcing
- Deal sourcing & pipeline screening
Surface and rank candidates from signals no database holds — conviction stays human.
- Data-room & due-diligence first pass
A first pass across thousands of files in minutes — the verdict on the deal stays human.
- Investment committee memo draft & fact-check
Draft the descriptive sections and trace every figure to the model; the thesis stays with the deal team.
AI in portfolio management
- Portfolio-company reporting roll-up
Harmonise mismatched portco packs into common KPIs — the step Power BI can't do alone.
- Covenant & portfolio monitoring (credit)
Extract terms with AI, test with rules — and keep the compliance test deterministic.
- Private-asset valuation support
Assemble the valuation pack and tie every input to source; the committee keeps the fair-value call.
- ESG & SFDR data collection
Read portfolio-company returns into the PAI indicators, with every estimate labelled and sourced.
Prompt library
Each use-case page carries a prompt you can run in your firm's approved AI tool. Every prompt follows the same six steps, so the output can be checked the way a finance reviewer checks any working paper:
- 1Map the sourcesWho produced each file, as at which date
- 2Trace the preparationHow each figure got from the original document into the file
- 3Match and reconcileShared IDs first, then standardised names
- 4Build the linked workbookEvery number a formula back to its source
5. Controls (must pass)Every row accounted forEvery figure and quote traced to its documentEvery number a formulaChecks written for each jobFour-eyes sign-off
6. Measured over timeBack-test finds every known difference0 corrections after sign-offCycle time vs manualRolls forward with 0 manual edits
- Controls that must pass: every row is accounted for, every derived number is a formula, and every figure or quote is traced to its document and agrees with it. Totals are tied only where the job has totals. Any miss is flagged at once and blocks sign-off until it is resolved; an explanation alone does not clear it. A preparer and a different approver sign. The rest of each page's checks are written for that job: a phone callback on changed bank details for capital calls, quorum and resolution wording for board minutes, answer age for a DDQ.
- Measured over time: a back-test on a past period must find every known difference. After that, track corrections after sign-off (target zero), reviewer corrections, false flags and cycle time against the manual process.
- Reusable: next period, replace the source tabs and the workbook recalculates. The name-matching table carries forward.
- Auditable: a log of every source file, rule and assumption, with source data never overwritten.
| Job | What the prompt produces |
|---|---|
| NAV break & reconciliation triage | Holdings reconciliation, administrator vs manager, with an exceptions list |
| Capital-call & distribution notice processing | Notices read into a cash-flow schedule tied to each investor's commitment |
| Investor onboarding & AML/KYC chasing | A document-gap register per investor, tied to the checklist and the subscription form |
| Annex IV & regulatory report assembly | A field-by-field tie-out of the draft filing to the source books |
| Side-letter obligation extraction | An obligations register with clause references and an MFN comparison |
| DDQ & RFP answering | Draft answers matched to the approved answer bank, each with its source |
| Board packs & minutes first drafts | A draft minute and action log, each line tied to the pack or the notes |
| Fee & carry verification vs the LPA | An independent fee recalculation linked to the LPA terms and the charged amounts |
| Investor-services inbox triage | A classified and routed inbox log, each item tied to the register |
| Closing-set consistency review | A term-by-term comparison across PPM, LPA and subscription documents |
| Deal sourcing & pipeline screening | A screened pipeline with every score tied to a cited signal |
| Data-room & due-diligence first pass | A data-room index and issues list, each issue tied to a document and page |
| Portfolio-company reporting roll-up | Portfolio KPIs mapped to the fund's definitions, each tied to the company pack |
| Covenant & portfolio monitoring (credit) | Covenant tests computed by formula from quoted definitions and borrower accounts |
| Fund financial statements tie-out | Every figure in the draft accounts tied to the trial balance, holdings report and prior year |
| Fund expense allocation review | Each invoice line classified by LPA clause, with the expected split against the booked split |
| Year-end audit PBC support | A PBC tracker with status, owner and ageing, and supplied schedules tied to the trial balance |
| CRS & FATCA investor classification | Forms read, indicia tested and a draft reportable-account list, all formula-linked |
| Quarterly investor report drafting | A fact-check workbook tying every figure in the draft letter to the capital accounts and fund data |
| Regulatory change mapping | Obligations mapped to policies and controls, with a gap register of owners and dates |
| Delegate & service-provider oversight | SOC 1 deviations and user-entity controls mapped to owners, with SLA breaches and a proposed rating |
| Investment committee memo draft & fact-check | A draft of the descriptive sections, with every figure traced to the model or a report page |
| Private-asset valuation support | A linked valuation workbook with comps, entry calibration and movement attribution |
| ESG & SFDR data collection | PAI indicators per company, each marked reported, estimated or not available, tied to source |
Field notes
Short notes on the everyday ways AI already helps at a fund desk: one practical move each, what it saves, and the check that keeps it safe. They are practitioner experience and general industry practice, not graded evidence, and they name no employer, client or deal.
- You don't need a new tool. You need AI to teach you the one you already have.
Point AI at the vendor's own documentation to learn your task the reusable way, then ask which features you aren't using.
More on Fund AI
- Supplier landscape: named suppliers compared by workflow, data needs and control boundary, plus the latest vendor releases.
- AI news for funds: the weekly edition, AI deployed by fund firms, and model and lab news.
- AI regulation: what regulators expect from fund firms using AI, including the EU AI Act dates.
In practice: start from the job, not the tool. If a page shows the job is mainly a rules problem, a deterministic fix usually costs less and is easier to audit than an AI product.