AI cost modeling for financial institutions
Model client service, KYC, research and credit workloads with editable assumptions and see cost per successful outcome, break-even volume and sensitivity. Built for banks, asset managers and the teams inside them deploying AI.
Learn
Interactive scenarios set inside a fictional UK bank and wealth manager: client service, disputes, product terms, KYC, credit papers. Models, tokens, retrieval, agents, controls and AI economics, decided the way a capital committee decides, with consequences for budget, quality and risk.
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Cost per successful outcome, full workflow cost, break-even, payback and three scenarios, with every assumption labelled by evidence type and stress-tested, so you know which numbers your business case actually rests on.
Analyze a use caseSee BillingEngine in action
A fictional UK retail bank handles 1 million customer conversations every month. It wants an AI agent to resolve routine requests and escalate complex cases to humans. BillingEngine models the entire workflow, not just the token bill.
Illustrative example, not an industry benchmark. All assumptions are editable and are not industry benchmarks.
Live preview, assumptions moving
State 1 of 4Largest modeled cost right now: Human escalation.
Banks and insurers
Client service, disputes, KYC, complaints and operations, where cost-to-serve is measured and regulated.
Asset managers and hedge funds
Research, reporting and client communications, where head-count leverage is the whole argument.
Private equity and investors
Test an AI thesis in a portfolio company before it becomes a value-creation slide.
Internal AI and transformation teams
Take a defensible number to the capital committee instead of a vendor's token price.
What does this AI workflow cost per successful outcome?
Which assumptions actually drive the business case?
When does it break even, and on what conditions?
What must we validate before releasing capital?
Insight
Every piece carries a live, editable model rather than a static chart, including our recurring flagship report on the state of AI economics in financial services.
A recurring flagship report. Edition one: what public research establishes, what it does not, and modelled scenarios for the four workloads financial institutions are funding most.
AI economics
The Real Cost of AI Agents in Financial Services
11 min
AI economics
AI Agents in Banking: From Tokens to Cost per Customer Outcome
10 min
Investment case
How to Build a Business Case for AI in a Bank
12 min
Architecture
RAG for Financial Services Leaders
10 min
AI economics
AI Agents for Investment Research: What Actually Drives Cost?
10 min
Investment case
Build vs Buy AI in Financial Services
11 min
BillingEngine separates your inputs from our estimates, shows the evidence type behind every number, and reports scenarios and ranges rather than false precision. The arithmetic is deterministic and unit-tested, no language model touches a financial calculation.
See methodologyNo. BillingEngine produces scenario models, not forecasts or recommendations. Outputs are ranges built from your inputs and clearly labelled assumptions. Decisions about capital allocation, vendors and compliance remain yours, and the terms of service state this plainly.
Three places, always separated: values you enter, BillingEngine estimates (visible, editable defaults when you answer 'I don't know'), and deterministic arithmetic. The calculation engine is unit-tested; no language model touches a financial figure.
Anonymous sessions stay in your browser. If you create an account, your assessment inputs are stored so you can return to them, and are never used to train models or shared with advertisers. The privacy policy covers this in detail.
Yes. Every default is editable on the results page, including model pricing, labour cost, volumes and automation targets. The sensitivity view shows which of your inputs the answer actually depends on.
Your report is a structured business case: scenarios, sensitivity ranking, confidence and the assumptions behind every number. You can keep it in your account and share it internally, for example with a capital committee.
The AI Academy and AI Economics Snapshot are free. One AI Investment Case is £2,500 per use case. An expert-led AI Investment Assessment starts from £10,000 per use case. See Pricing for exactly what each level includes.
Start with the Academy if you want the foundations. Go straight to an assessment if you already have a workload in mind.
