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Fund Accountant

Calculate management and performance fees

Automates✓ Available Now

What You Do Today

Compute management fees, performance fees (including high-water marks, hurdle rates, and crystallization), and other fund-level expenses according to offering document terms.

AI That Applies

AI calculates fees using complex fund terms, tracks high-water marks and crystallization periods, and validates calculations against prior periods and offering documents.

Technologies

How It Works

The system ingests high-water marks and crystallization periods as its primary data source. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What Changes

Fee calculations become more automated and auditable. Complex multi-share-class, multi-series calculations process without manual intervention.

What Stays

Interpreting fee provisions in ambiguous situations — and resolving fee disputes with investors or managers — requires legal and accounting expertise.

What To Do Next

This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for calculate management and performance fees, understand your current state.

Map your current process: Document how calculate management and performance fees works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Interpreting fee provisions in ambiguous situations — and resolving fee disputes with investors or managers — requires legal and accounting expertise. These are the boundaries AI won't cross.
Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support fee calculation engines tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long calculate management and performance fees takes end-to-end today, then after AI adoption.

Why it matters

The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.

Quality of output

How to calculate

Track error rates, rework frequency, or stakeholder satisfaction scores before and after.

Why it matters

Speed without quality is just faster mistakes. Measure both.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

Start These Conversations

Who to talk to and what to ask

your CFO or VP Finance

What data do we already have that could improve how we handle calculate management and performance fees?

They're prioritizing which finance processes to automate first

your ERP or finance systems admin

Who on our team has the deepest experience with calculate management and performance fees, and what tools are they already using?

They know what automation capabilities exist in your current stack

your FP&A counterpart at a peer company

If we brought in AI tools for calculate management and performance fees, what would we measure before and after to know it actually helped?

They can share what worked and what didn't in their AI rollout

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.