Independent analysis of AI for payments and finance operations
We help payments and finance leaders decide where and how to use AI, using model tests to work out the economics.
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Our research agenda
Firms now pick AI models on workload fit, cost, latency and compliance. The cost they compare is usually the token price. ScalePoint Research adds what each model's mistakes cost, by testing models on real payments and finance tasks from a map of 51 processes.
For each model, the tests show:
Workload fit
Accuracy on the task and the cost of mistakes, ranked across the tests.
Cost
The token cost per task, shown next to each model's score.
Compliance
The hosting routes that keep no copy of the data.
Each test's Excel model, available on request, adds checking and fixing costs and works through routing scenarios. How the tests work →
What the tests show so far
Every chart below comes from the live leaderboards and updates when a test does. Enter research for every result.
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
The wider agenda. Beyond the tests, ScalePoint Research covers investment prioritisation, budgeting, forecasting, cost management and value realisation for AI in payments and finance operations.
- 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.
Email martin@scalepointpartners.com or connect on LinkedIn.