Launching the ScalePoint Strategic Finance Toolkit for Payments and Fintech — Starting with Pricing Sensitivity
Practical web apps and Excel tools for pricing, unit economics, and structural diagnostics
In Brief
Launching today: the first in a portfolio of strategic finance tools for payments and fintech operators — starting with a Pricing Sensitivity tool that shows exactly what a take rate concession costs you in margin dollars before you agree to it.
This and other forthcoming web apps are visual and interactive. Some integrate AI for deeper, contextual interpretation. Others are purely deterministic. All are built in Next.js.
Excel models will be clean, documented, and ready to use without a handover.
The tools sit at the intersection of external insight and internal financial reality. Pricing, unit economics, structural diagnostics.
A direct response to Excel sprawl and the half-finished models that become useless the moment the person who built them walks out the door.
Built on the idea that AI can standardise the intelligence layer, leaving judgement where it belongs: with humans.
If you want to build something similar, I am happy to help you bring it to life.
In Detail
Over the past several months I have been building a portfolio of strategic finance tools aimed at founders, investors, and finance operators working in payments and fintech. The first is launching today. More will follow.
The portfolio combines Excel models and web applications. Different formats for different contexts, but a consistent point of view running through all of them.
About the tools
The topics sit at a specific intersection: external insight applied to internal financial reality. Pricing decisions do not happen in a vacuum. They are shaped by competitive dynamics, customer behaviour, and structural unit economics all at once. These tools are built for that intersection.
The Excel models are built cleanly: strict separation of inputs, calculations, and outputs, with enough supporting documentation that someone can pick one up without a handover call.
The web applications are more intuitive by design — sliders, clear input fields, and visual charts that update in real time. Some are single-screen; others walk through a diagnostic in sequence. Some integrate with AI for interpreted insights; others are purely deterministic. They are built in Next.js, which guarantees fast load times, clean rendering, and an interface that behaves like a modern web product. The codebase is clean and straightforward, so easy to reskin and rebrand for your own context — something I can help with if useful.
I am happy to share these tools. The Excel models are immediately downloadable. The web applications are open to fork and run independently, or I can walk you through how to get set up.
The first tool: Pricing Sensitivity
Most fintech businesses run on thin spreads applied to large volumes. The economics look manageable until someone asks for a pricing concession, and the margin impact turns out to be larger than anyone expected.
The Pricing Sensitivity tool makes that dynamic visible in real time. You enter four parameters — transaction volume, take rate in basis points, network and infrastructure costs, and partner revenue share — and the tool calculates your contribution margin and plots how it changes across the full range of take rates from 10 to 200 bps.
The key feature is the baseline. You set your current parameters as a reference point, then adjust any variable to see the delta immediately. A 10 bps concession at $500M volume costs more than it looks. This tool makes sure you know exactly how much before you agree to it.
It is deliberately simple. It does not model demand response or volume elasticity — volume is held constant. The purpose is to isolate the pure margin impact of a pricing decision, which is usually the conversation that needs to happen first.
The tool is free to use at tools.scalepointpartners.com/pricing-sensitivity.
The problem these tools respond to
The problem most people in fintech already know: Excel sprawl. Models built in a hurry for a specific purpose, never quite finished, never properly documented. When the person who built them leaves, the understanding goes with them, and what remains is a file nobody fully trusts. This is not a new problem. But having a cleaner alternative readily available — already built, already tested, ready to use — removes the friction that usually leads people back to starting from scratch.
There is a deeper idea here too. The possibility of separating strategic finance work into two distinct layers: intelligence and judgement. The intelligence layer is the structural work — building the right model, running the right scenarios, surfacing the right numbers. That layer can be standardised and increasingly accelerated with AI. I have used AI heavily in building these tools and it has made a real difference to how quickly something rigorous can be produced. The judgement layer — what does this mean, what do we do about it, what do we defend in a board meeting — stays human. These tools are an attempt to draw that line clearly and invest properly on both sides of it.
If you want to build something similar
If any of this resonates and you are thinking about building something similar — whether that is a single diagnostic model or a more sophisticated tool — I am happy to talk through how to approach it. I have learned a lot about what works in building these, and I am glad to help others architect something that actually gets used.