Independent analysis of AI for payments and finance operations
We help payments and finance leaders decide where and how to use AI, and work out the economics.
Our research agenda
ScalePoint Research helps payments and finance leaders pick the right AI model for each step of a process, and know what it will cost and where the data goes before they build or buy. The work covers tests, studies, economic models and demos on real tasks from a map of 51 processes, such as reconciliation, chargebacks and merchant onboarding.
Each piece of work looks at three things:
Workload fit
What a model can do on the task.
Cost
Everything it costs to run, including errors.
Compliance
How the model's use is governed, covering data, access and regulation.
What the tests show so far
The charts below use the same data as the test pages, so they update when a test is re-run. All published research →
What's new and what's being worked on →
From test result to economic model
Each test has an Excel model, available on request, that turns the results into cost per task, annual cost and payback for a given firm.
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Articles
Reading Card Statements
What Test 1 found when 34 models read UK card acquiring statements, which faulty statements they caught, and what each model costs to run.Read → Research · Model testsTest Design: What the Model Does and What Code Does
Which steps the tests give to a model, which they leave to code, and the judgement skills they score.Read → Latest test · Test 2Reconciling Settlements
What Test 2 found when 11 models reconciled a month of card and payout settlements, and how far apart the models were.Read → AI economicsThe Unit Economics of AI in Payments and Finance Operations
What an AI model really costs per case once checking, mistakes and fixed costs are counted.Read →Custom research and advisory
ScalePoint Research also works with firms directly on their own use cases, through custom research, advisory projects and executive briefings. The work covers five areas:
- Economic models. A model of a use case's cost structure, unit economics, payback and scenarios, with one-off investment kept apart from recurring costs and forecast benefits kept apart from measured ones.
- Build, buy, combine, scale or stop. Whether to build, buy or combine for a given process, which deployment option fits, how to fund it, and when to scale, redesign or stop a use case.
- Model and Vendor Check. Private evaluations of AI models, or a vendor's AI product, on your own cases: accuracy in money, cost per task and where failures sit, plus acceptance tests for the contract.
- Pricing intelligence. Price points for models, hosting and vendor products, and the commercial terms that change cost at scale, built into the economic models.
- Value realisation. How adoption, workflow change, output quality, human oversight and risk affect the cost and the value actually delivered, checked against the business case for live use cases.
About
Martin Koderisch is the founder and lead analyst of ScalePoint Research. He leads the research on the economics of AI in payments and finance operations, and advises payments and fintech businesses on strategic finance, unit economics and value creation.
He spent ten years in operating roles at Citi, Mastercard and the start-up Luup, then nine years at Edgar, Dunn & Company, latterly as a Principal, on more than 75 engagements for banks, fintechs, card networks, private equity firms and merchants. Since 2024 he has worked independently with payments and fintech businesses on strategic finance and value creation. He splits his time between London and Barcelona.
You can also connect on LinkedIn.