RIGFOA builds AI-assisted governance tools for the officials who steward public money — fiscal risk monitoring, compliance checking, spending anomaly detection and multi-year budget analytics — and publishes the methodology behind all of it.
Sample interface. Entity names and values are fictitious and shown to illustrate the display only.
Every output resolves to the records that produced it. If a result cannot be traced to a transaction and a policy provision, it is not something a public official can act on.
Continuous fiscal indicators — structural balance, liquidity, reserve adequacy and long-term obligations — computed from the entity's own ledger.
Explore Risk MonitoringGASB and GAAP checks with a working paper trail. Lease and SBITA registers that stay current instead of being rebuilt each year.
Explore Compliance DashboardTransaction triage for post-payment review, pairing statistical ranking with the policy provision each flag implicates.
Explore Fraud DetectionDriver-based multi-year forecasting with published assumptions, three scenarios and a sensitivity table that shows what the answer depends on.
Explore Public Finance AnalyticsA three-person finance department does not have a prediction problem. It has the same volume of documents it had twenty years ago plus a decade of new reporting obligations, and the same three people.
That reframing changes what a useful tool looks like. Software that produces an estimate replaces judgement a department already exercises reasonably well. Software that ranks nine thousand transactions so the forty worth reading appear first delivers judgement to where it is scarce. We build the second kind.
Read our methodologyFrom special districts with a two-person finance function to counties with a full internal audit programme. Partner and member organisation marks appear here as we publish them.
Twenty-five publications across AI governance, municipal budgeting, fraud prevention, GASB and GAAP compliance, public-private partnerships, sustainable finance and digital transformation.
The failure pattern is consistent across entities of very different sizes, and it is established during procurement rather than during implementation.
Most of the value from machine learning in local government finance is not in prediction. It is in triage — deciding which of ten thousand transactions a human should look at first.
A balanced budget and a structurally balanced budget are different claims. Telling them apart takes about twenty minutes and four adjustments.
Walk through the platform with your own chart of accounts, or start with the research library. Both routes are free to begin.