02State Tax & Revenue
Refund-fraud defense and revenue-system modernization that pay for themselves.
State tax agencies are the largest revenue-side IT systems most state IT shops operate. They also sit at the largest fraud surface. Refund-fraud rings, false-filing schemes, and identity-theft refund attacks have cost states billions over the last decade. The detection layer is the one that pays for the modernization.
- Annual state tax collections
- $1.1T+
- Refund fraud, annual loss
- $1–3B
- Identity-theft refund attempts
- millions / yr
01What we keep seeing
Three failure modes that pre-date the AI conversation.
01
Refund fraud detected after the refund leaves
Most state tax revenue agencies run identity-theft detection as a batch process on returns already accepted. The IRS sees the same return through different lenses; the state sees it once, post-pay.
02
Single-return scoring misses ring activity
Fraud rings refile across multiple TINs at the same address, the same bank account, or the same prep service. Single-return models can't see the ring. Graph signals over claimant + bank + preparer can.
03
Adverse-action notices are unreadable
When a refund is held, the notice generated rarely tells the filer what to do. Hold queues fill with legitimate filers who got bad letters; fraudsters meanwhile re-attempt with adjusted submissions.
02How we work the seam
Specific practices. Specific outcomes. No platitudes.
Practice 01
Outcome
Move detection to before the refund check is cut
Pre-issuance refund-fraud scoring
Move the fraud signal from post-issuance audit to pre-issuance scoring at the moment the return is e-filed. Graph features over claimant ↔ bank account ↔ preparer ↔ address surface ring patterns single-return scoring misses.
Practice 02
Outcome
Block synthetic-identity returns at submission
Identity-theft refund-attempt detection
Signals on filer behavior, device fingerprint, return composition, and credit interactions detect the synthetic-identity refund attempt before a hold is needed. Pairs with IRS data-sharing where available.
Practice 03
Outcome
Reduce legitimate-filer churn in the held-refund queue
Notice readability and appeal pathway
Rewrite the held-refund notice with counsel review, plain language, and a clear path back to release. Pulls down false-positive friction without weakening the detection layer.
Practice 04
Outcome
Per-determination reconstructibility
Provenance for OIG defense
Every refund-hold decision must carry the features, model artifact, and policy text that produced it. State OIG and the AG's office routinely audit these, and the trail decides whether the program survives review.
03Market scale
The size, growth, and obtainable share of this market.
Every benefits-decisioning segment shares the same pattern: program dollars in the hundreds of billions, services spend in the single-digit billions, modernization and detection investments compound across the program spend.
TAM
$1.1T
Program dollars in scope
SAM
$2.4B
Annual services spend
5-yr CAGR
7.2%
Rolling SAM growth
3-yr SOM
$65M
Vardr-obtainable
Vardr captures a small but compounding share of the addressable services spend; the underlying program dollars dwarf the modernization budget.
Federation of Tax Administrators state collections totals. SAM = state DOR modernization + refund-fraud defense services. SOM = 3-yr Vardr-obtainable at current scale.
5-year SAM growth: State Tax & Revenue
$2.4B → $3.4B
04Programs we focus on
State tax and revenue agency modernization.
State Department of Revenue
Personal income tax refund processing
Returns, refund issuance, identity-theft holds, appeals workflow.
State Department of Revenue
Earned Income Tax Credit (state-level)
State EITC refund-fraud exposure, cross-referenced against federal EITC and SNAP eligibility data.
State Department of Revenue
Sales and use tax modernization
Marketplace facilitator collection, audit workflow, remittance-reconciliation tooling.
State and county Department of Revenue / Assessor
Property tax assessment review
Anomaly detection on assessment patterns, appeal queue triage, valuation drift signals.
05Questions worth asking
Open these with us, or with anyone else.
We bring these to every program-office conversation. Use them whether or not we end up working together.
- 01
Of refund-fraud losses last fiscal year, what share was detected pre-issuance vs post-issuance?
- 02
Can your fraud signal join claimant identity to bank account to preparer to address, or only to one of those?
- 03
What does the held-refund notice say, and has counsel reviewed it this fiscal year?
- 04
For a refund hold flagged eight months ago, can you reconstruct the exact features and model artifact that triggered it?
06Related insights
Working in state tax & revenue and want our take?
45-minute principal-level briefing. Bring the program, the constraint, the deadline.