FinTech · AI-Assisted Private Equity Investing · 2025
- Role
- Lead Product Designer
- Surface
- AI for private-equity investing
- Screening · n=42 · 90-day window
- −60% time
- Status
- Shipped · under NDA
Top customer is 38% of recurring revenue — concentration pushes the score up.
Margins hold steady across three years; working capital reads clean.
Churn figure couldn't be matched against the raw export.
“I'm not sure about this one” — low confidence, kept visible
Disagreement logged — training signal
I held the release until the model could defend its own scores. Screening ran 60% faster once it could. Client is under NDA — I'll walk through the methodology on a call.
PE analysts are paid to be suspicious of confident numbers. Doubt is the job, and they're good at it. A score they couldn't cross-examine wasn't a tool — it was a liability with a UI, and they treated it like one.
The model scored deal risk accurately. That was never the bottleneck. The bottleneck was that every score read the same on screen whether the model was ninety percent confident or fifty percent guessing. With no way to check the reasoning, analysts built parallel scorecards and let the AI rank as a sanity check at best.
So I built an "explain this score" surface. An analyst could pull any rating apart into the signals behind it, challenge the weighting, and watch the score answer back. I held the release until the model could survive that. Three things were non-negotiable: the primary sources behind each number, a visible "I'm not sure about this one" state for low-confidence cases, and a logged override when the analyst disagreed. The override drew the longest argument — logging disagreement felt exposing. What it actually built was the training signal that sharpened the model over time.
Interrogation turned into reliance. Screening ran 60% faster across 42 deals in a 90-day window, and analysts went from ignoring the model to leading their deal memos with it. The model didn't get more accurate — they could finally see where its confidence came from.
Want the full version of this work?
Hiring? In an interview I'll walk you through the decisions, the artifacts I can't host publicly, and the numbers — under mutual NDA.
Send me the roleDesign Patterns Used in This Case
This project produced two patterns I now reach for whenever AI meets domain experts:
- ML Explainability Patterns: How to expose a reasoning chain — from the steps the model took to how this deal stacks against its peers — so a non-technical expert can read the why, not just the verdict.
- Human-in-Loop Patterns: The override-and-explain loop, the visible weighting on each risk factor, and disagreement handled with respect — the moves that keep the expert in command.
I write about this on The Trust Layer ↗