This is a product concept I came up with after reading investor stories online. It's not built yet. This case study is about the thinking behind it.
At Amundi I helped build NarrativeAI — a tool that writes wealth reports for advisors. While working on it I kept noticing something: advisors struggle to explain things clearly to clients. The reports help. But I got curious about the other side — what are clients actually experiencing?
So I spent a few hours reading investor forums. Reddit, Bogleheads, PortfolioPilot. People sharing stories about their financial advisors. What I found was pretty bad.
"My adviser recommended I take out a mortgage on my fully paid-for home to buy a variable annuity. Everybody would be taking their cut — the mortgage broker, the annuity company, her — and I would be last in line for value."
Reddit, r/personalfinance"I asked for a simple projected income statement for post-retirement. He worked for two weeks and said he just couldn't produce one."
PortfolioPilot horror stories"She put me in 20 different mutual funds — diworsification. Every single one paid her a sales load. No real diversification for me. Just fees for her."
Bogleheads forum"They pushed Whole Life Insurance as an investment vehicle. Great commissions for them. The numbers don't lie — but they never showed me the numbers."
PortfolioPilot horror storiesAll different stories. Same root: the client didn't have the same information as the advisor. They found out too late.
You could say the problem is bad advisors. But I don't think that's quite right. Most advisors probably aren't bad people. The problem is the model: they get paid more when clients pay more in fees, not when clients do better. That's the real issue.
If you look at it that way, the solvable piece is the information gap. Clients don't know what they're paying. They don't know when an advisor has a conflict of interest. They can't get a plain-English explanation of their own portfolio. I wanted to fix that.
Here's the before and after I was thinking about:
| What clients deal with today | What Claro would change |
|---|---|
| Don't know the total fees they're paying | All costs visible in one screen, in euros |
| Can't tell if a recommendation earns the advisor a commission | Commissions flagged automatically, before signing |
| No plain-English explanation of their portfolio | Any question answered in plain language in under a minute |
| No independent source to check advice | Conflict-free second opinion, always available |
I also decided early on what Claro would NOT do — no trading, no portfolio building, no replacing advisors. That's a different product. Claro is just the information layer. Give clients what they need to make informed decisions with the advisor they already have.
Two groups came up in the research. I'd build for the first one first.
Group 1 — Someone who already has an advisor and suspects something is off. They're paying fees but don't know how much. Their advisor recommended something recently and they're not sure why. They don't have the time or confidence to dig into it themselves. This is the person who needs Claro the most, right now.
Group 2 — Someone who manages their own investments through apps like Scalable Capital and wants a better overview. Lower urgency, easier to serve later.
I'd focus on Group 1 first because their pain is immediate. An advisor is actively recommending things. The moment they see a hidden commission flagged — before they sign anything — that's a powerful, memorable product experience. That's what earns loyalty early on.
The deepest pain in the research was "I didn't know what I was paying." Fee visibility has an immediate outcome — you see a number in euros and you can act on it. AI chat is better once you have good data underneath. I'd build the data layer first, then put the AI on top of it.
I had a version where users could "check" their advisor. I scrapped it. If someone has to go looking for a red flag, they probably already suspect one. The whole point is to catch things they wouldn't think to check. The app has to do it for them.
The product's whole promise is "no conflicts." If Claro took referral money from product providers, that promise falls apart. A subscription at around €15/month works fine for someone managing €100K+. They'd save far more than that in the first year.
The "aha moment" is just seeing your fees for the first time. That needs one thing: link your account and show the cost. I'd ship that first, get users to that moment, and only then build the rest.
I designed four screens — one for each key thing the app does.
Three metrics I'd track from day one:
Not just "viewed the fee screen." Did users take action and cut their costs? If not, the product is informing but not helping. Something in the flow is broken.
The moment where someone sees their fees for the first time has to happen fast. If connecting an account takes 10 minutes we've already lost them.
If people don't come back after the first week, it was a curiosity, not a product. Above 40% means there's a reason to return. Below that, something is missing from the core loop.
I wouldn't track NPS early. It tells you how people feel, not whether the product is actually working.
Show 6–8 people the fee screen and ask what they'd do next. Not "do you like it" — "what would you actually do after seeing this number?"
Connect Scalable Capital (Open Finance API). Show fees only. No AI, no conflict detection yet. Just get users to the aha moment and see if they stay.
Commission data for German-market products. Flag recommendations automatically. This is the part that's hard to copy — it takes real data work.
Only once the data layer is solid. AI on bad data is just a confident way to give wrong answers.
I built NarrativeAI to help advisors explain things better. I thought the problem was communication. But doing this project made me realise I was only looking at one half of it — the advisor's side.
Claro is the other half. The client side. And the real problem isn't that advisors explain things badly. It's that clients are making big financial decisions without the information they need. Fixing that is a product problem. That's the kind of problem I want to work on as a PM.
Case study by Yuvaraj Devadoss
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