Most of our Central engagements start the same way: a 20-minute call where the owner describes a workflow we've heard before in shape but never in detail. AI fraud detection is then designed against the detail, not the shape.
For Central operators who want a working pilot in weeks, not a year-long programme.
Our goal is to give Central businesses a three-day weekend, so people can spend more time with their families and the people they love :)
What AI fraud detection actually does
Pattern-watching AI for refund abuse, chargebacks, fake reviews, employee fiddles, and odd supplier invoices. Flags weirdness early - before it's a real problem.
- 01 Learns your normal patterns and flags outliers
- 02 Daily anomaly report, not a constant alert flood
- 03 Explainable scoring so you can act with confidence
- 04 Integrates with Xero, Shopify, and POS systems
Built on: Claude DuckDB Postgres Vercel
Without Kiwi Dynamics
With Kiwi Dynamics
What you actually get
Every engagement is scoped and quoted up front. This is what is in the box.
- A working pilot, in productionNot a prototype on someone's laptop. The first slice of AI fraud detection runs against real work within weeks.
- Your data stays yoursIt runs on your accounts and your tools. If we part ways you keep the system and everything in it.
- The workflow mapped before codeWe write down what good looks like for Central businesses first, so nobody is guessing at handover.
- Support after it landsThe people who built it stay reachable when the business changes shape around it.
How AI fraud detection compares
The two things most businesses do instead, and where each one runs out.
| Hiring for it | An off-the-shelf tool | Kiwi Dynamics | |
|---|---|---|---|
| Fit to how you work | Fits perfectly, costs a salary | You bend your process to suit the tool | Built around the workflow you already run |
| Time to something useful | Immediate, and permanent | Quick to switch on, slow to make fit | A working slice in weeks, then hardened |
| Who owns the data | You do | The vendor, on the vendor's terms | You do, in your own accounts |
| When it breaks | That person sorts it, if they are in | A support queue and a ticket number | The people who built it |
| What it costs | A salary, every year, forever | Per seat, forever, used or not | Scoped and quoted up front |
Where Central operators actually lose hours.
- Central is Hong Kong's financial core, home to the regional headquarters of most major global banks - AI here has to meet a market built on precision, compliance and speed of execution.
- Hong Kong's stock exchange, the regional HQs of the world's biggest banks, and a dense wealth management and private banking sector all sit within a few blocks. Businesses here expect AI that's compliant-by-default and genuinely production-ready, not a pilot.
We work with teams across Central: Admiralty · Sheung Wan · IFC · Mid-Levels · Wan Chai.
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How we build AI fraud detection for a Central team.
We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for your Central business, so value lands before the build is finished. AI fraud + anomaly.
Talk to usHow the work runs
- Find the one workflowWe look for the job costing Central businesses the most hours or the most leads, and deliberately ignore the rest for now.
- Ship a working sliceA narrow version goes into production in weeks, against real work, so the value shows up before the build is finished.
- Prove it, then widenWe measure it against what the work cost before. If it does not pay for itself, we say so rather than scaling it.
- Harden and hand overLogging, fallbacks and a real handover, so it keeps running when we are not in the room.
The outcome for Central teams
If we build the right slice first, Central teams feel the difference inside the first month. Recovers 3-5x its cost in caught fraud within 6 months.
Not your typical AI agency.
Honest about what AI can and cannot do
Ships the one workflow that pays for itself
Hours given back, never the size of the invoice
*Every engagement is scoped and quoted up front. Results vary by workflow and business.
FAQ
How long does AI fraud detection take to ship for Central businesses?
We aim for a working pilot inside 4-6 weeks - narrow scope, real Central businesses data, measurable outcome. From there it's another 6-8 weeks of hardening before you'd consider it production. Full rollouts (multiple sites, multiple teams) typically land in 3-4 months.
How do you price AI fraud detection engagements?
Fixed-scope pilots first, then either project pricing or a small monthly retainer for the ongoing work. No long lock-ins, no 18-month black-box deals. Most Central businesses are surprised how small the first cheque is.
Anyone else in this space using AI fraud detection?
Plenty. Recovers 3-5x its cost in caught fraud within 6 months. The interesting question is rarely "does it work" - it's "is your team ready to use the output." That's what we'd scope on the call.
Who owns the code and the model setup?
You do, on delivery. We deploy AI fraud detection into your own cloud account where possible, with the model setup, prompts, evals and integration code all checked into a repo you own. Claude sits in your account too - we don't operate it from ours.
Worth a conversation?
Even if you don't end up working with us, you'll leave the call knowing what's worth building.
Talk to us about this
Tell us what you're trying to do and we'll reply with how we'd build it - no obligation.