Know where AI fits, and what to buy.
Map the change against your business, choose the technology for it, and see how your industry is moving.
Finantrix shows financial institutions where AI fits in their business, what technology to buy, and how their industry is moving, and gives the vendors and builders who serve them the reach and the data to keep up.
* In the Banking and Payments Capability Model, also sold on its own.
Map the change against your business, choose the technology for it, and see how your industry is moving.
Show up correctly where institutions compare vendors, and target the firms most likely to buy.
Synthetic financial data and evaluation packs, so real customers' data stays out of your test environment.
Whether the change is an agent in the contact center or a new core, the questions come in the same order. Each one has a part of Finantrix behind it.
Capability models, value streams and data models that show each capability's AI profile and risk, and each process today and AI-native.
Independent buyer guides by software category, ranked comparisons, and a directory of the vendors behind them.
Feature lists to specify the system, and synthetic data to test it without real customers' data.
Peer benchmarks and market data from regulators' filings, weekly data stories and analysis.
The people reading the buyer guides are choosing a vendor in your category. Claiming your listing costs nothing; visibility and go-to-market data are paid.
Claim your listing, correct it, and route buyer inquiries to your team.
Claim your listing →A labeled slot on your category's buyer guides, reviewed before it runs.
Where filings name vendors, we read them: advisers' archiving, compliance, custody, planning and reporting; funds' administrators, custodians and auditors; and every new SEC-registered adviser.
How placements work: our editorial policy.
A model that advises households has to be tested on households. Synthetic data gives you realistic ones, built for the test you need to run, with no real customer in your environment.
Synthetic household datasets, each built for a specific test. For example:
Tax-year-vintaged evaluation packs for AI financial-advice systems, to check whether a model gives this year's answer or last year's.
About the eval sets →Decision memory add-ons for the same synthetic households.
About DecisionSynth →Every figure names its filing and the date it runs through. Coverage follows what each industry has to disclose, so it is deeper in some places than others, and we say which.
| Sector | What we cover | Source | Depth |
|---|---|---|---|
| Banks | Peer benchmarks; size, growth, lending, enforcement | FDIC call reports, OCC, Federal Reserve | Deep |
| Credit unions | Peer benchmarks; mergers, growth, concentration | NCUA 5300 | Deep |
| Advisers | Peer benchmarks; new registrants; the vendors they name | Form ADV, advisers' websites | Deep, including vendors |
| Funds | Private funds and 13F managers; administrators, custodians, auditors | Form ADV, N-CEN, 13F, Form D | Deep, including providers |
| Broker-dealers, FCMs | Largest firms, customer funds, trading revenue | X-17A-5, CFTC | Good |
| Insurers | Listed carriers; Medicare Advantage and health insurers | XBRL filings, CMS | Thin so far |
Compare a bank, credit union, adviser, fund manager, insurer or REIT with its nearest peers: peer benchmarks.
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Footnotes reports what changed in the filings for your sector. Choose the editions you want; every issue has a one-click unsubscribe.