Independent analysis of AI for payments and finance operations

We research the economics of AI, from testing models on real tasks to cost at scale, value and build or buy.

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Our research agenda

AI is rapidly becoming part of payments and finance operations, both in vendor products and in tools firms build themselves. Each test takes one task from a map of 51 payments and finance processes and addresses three questions about each model. How the tests work →

1

How well does each model do a given payments task, and what do its mistakes cost?

Each test has a leaderboard that ranks models on the money they get wrong.

2

What does each model cost per task?

Every leaderboard shows the model cost per task next to the score.

3

Where can each model run with zero data retention?

The hosting page shows the route each model ran on and whether that route keeps a copy of the data.

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.

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Articles

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

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.