For payments and fintech companies: see which accounts are quietly shrinking, what it costs you, and which ones are worth acting on, before it shows up in the monthly report.
Underneath: advanced statistics, causal inference and AI engineering, built into systems your team uses every day.
Clients include Fractal, Solventis and OmniXM.
A fictional book of 400 merchants. Each mark is one merchant, sized by the revenue it brings in.
Illustrative
Most reports stop at what happened. You also get the next two answers: what to do about it, and whether it worked.
Illustrative: 400 fictional merchants, one dot each. The 23 in red are shrinking faster than their normal pattern.
What is happening?
You see which accounts are shrinking, why, and what each one costs you, while there is still time to act.
Illustrative: each dot is a merchant, placed by how much its payment volume changed in the last 12 weeks. 23 are shrinking well beyond normal, putting $1.9M of revenue a year at risk. 14 more are only having their usual seasonal dip, so they stay grey.
What should we do?
You know which accounts are worth acting on and which to leave alone, with a dollar figure and an honest level of confidence. Predictive models, causal inference and optimization do the work underneath.
Illustrative: the 23 at-risk merchants, placed by what acting would earn after its cost. Acting on 9 of them is worth about $340K. For the other 14, the action would cost more than it brings back, so we leave them alone.
Did it work?
You see what each action actually earned, not what it seemed to, because we compare accounts that got it with similar ones that did not.
See how we measure itIllustrative: to check the action really works, 400 similar merchants were split at random. Of the 200 who got a rate review, 22 left (11%). Of the 200 who did not, 36 left (18%). The review kept about 7 more merchants in every 100, and the likely range is 1 to 13.
Then AI engineering keeps it running every day: the right person gets an alert, someone on your team approves each action, and every result sharpens the next decision.
Different problems, the same core skill: knowing what is likely to happen, what to do about it, and whether it worked.
See every model we buildBuilt for Fractal across payments, sales and operations, with predictive analytics, automated alerts and explanations written by AI.
Read the Fractal case studyFractal, construction fintech, United States. Figures from the case study.
Blinko delivered exceptional work on a highly complex problem. Deep expertise in data science, machine learning, and business domain knowledge – exceeded all expectations.
Nearly 100 client engagements delivered since 2023. Fiverr Pro and Top Rated.
Each step stands on its own. Most clients start with the diagnostic and go further only when it pays off.
How we work, step by step2 weeks, fixed scope
An assessment of your data, revenue trends and decision gaps. You get measurable opportunities, the limits of your data, and a practical next step.
One high-value decision, tested on your own history first. We agree up front how it must beat the way you decide today.
First version live in 4 to 8 weeks
The solution, deployed into your existing workflows with integrations, monitoring and alerts, and optional ongoing support.
2 weeks on your own data, with a written readout at the end. Built for payment companies and B2B fintech teams.
Start with a secure data export: transactions, billing or CRM. We assess feasibility before proposing any integration. NDA signed first.
No platform replacement you do not need, and no AI for the sake of AI. We start with the business question, test what your data can support, and build only what creates practical value.
Tell us what you are trying to understand or improve. We will help you work out whether your data can support a useful solution.
No pitch, just an honest answer. We reply within one business day.
