01 · Model risk
Read the model's paperwork
A guided tour of the file drawer — inventory entries, change logs, validator sign-offs — and the vocabulary that marks a drawer nobody opens.
A 6-minute read
Independent editorial · issued from Taipei
Falcon Protocol Review: how to check the AI that moves money
Algorithms now sit inside allocation decisions, screening queues and execution engines — and most writing about them is marketing in disguise. This desk reads the machinery itself: the governance paperwork behind investment models, and the humans, where they exist, who watch them.
Every article is a working checklist you can hold against a fund fact sheet, a robo-adviser disclosure, or a broker's claim about its newest neural engine. Nothing is sold here: no subscriptions, no courses, no commissions.
Editorial desk · +886 2 2765 4931 · info@falcon-protocol.digital
Reading paths
Three checklists to open first
Each one ends with the questions a governance disclosure either answers — or quietly dodges.
02 · Human oversight
Tune into the oversight chain
Five links — halt authority, escalation clock, override records, scheduled challenge, named accountability — each one easy to fake and easy to probe.
A 5-minute read
03 · The short check
Run the five questions
One meeting, five questions, no statistics degree required — with the shape a good answer takes and the smell a bad one gives off.
A 5-minute read
The desk checklist
How every article earns its place
Five steps, in the same order, whenever the desk sits down to write.
- 1
Pin the claim
Start by separating what the AI is said to do from what its documents say it does. Marketing adjectives go in the bin; the piece works from the precise decision the software is credited with making.
- 2
Pull the paperwork
Validation results, model inventory entries, re-test dates — or the silence where those should be. The paper trail decides how strong the piece can honestly be.
- 3
Map the humans
Who can override the model, who signs off on re-training, who answers for it when it drifts. If those names and roles cannot be found, that finding goes into the article.
- 4
Test the incentives
Who earns when the model is right, and who is left holding the loss when it is wrong. Fee structures are read as closely as performance figures.
- 5
Publish the residue
Each piece closes with what the disclosure leaves unreadable — the gaps a reader should carry away as open questions, not as reassurances.
Honest by design
What this desk is — and is not
Falcon Protocol Review is an editorial desk, not a finance operation. It publishes free articles about AI in investment — model governance, human oversight, and the questions worth asking before software is trusted with capital. That is the entire offer, and the whole business model in one sentence.
- Free to read, always — no paywall, no account, no newsletter you must trade your inbox for.
- Independent — coverage cannot be bought, and no affiliate link inside the articles earns the desk a commission.
- Inquiry-only — the contact form exists for questions, corrections and requests for future articles.
- Not personalised investment advice of any kind, for anyone, at any step.
- Not a brokerage, adviser or fund — no execution, no deposits, no product is offered or arranged.
Straight answers
Three questions before you write
Is any of this investment advice?
No. Articles explain how governance and oversight work in plain words; they do not size positions, name instruments to buy, or match products to readers. Where your own money is concerned, the right address is a licensed adviser where you live.
Do paid placements or affiliate links appear here?
Neither. No product earns coverage by paying for it, and no link in the articles pays a commission. When a tool or document is described, the source is public disclosure, and it is open to the same checklist as every other claim.
Can the desk look at one specific firm or fund?
As a topic for a future article, possibly. Send it in as an inquiry. The desk writes about patterns and public documents rather than issuing endorsements or verdicts about named products.