FinGuard
Catch financial misstatements before they cost billions.
FinGuard converts scarce, delayed misstatement labels into an explainable risk-scoring engine for SEC filings — helping regulators, auditors, and financial partners find hidden risk earlier and allocate oversight resources with precision.
Prioritize the highest-risk filings
White-box components provide per-instance explainability for targeted review and partner-ready trust.
Financial fraud is a needle-in-a-haystack problem with expensive consequences.
Audit teams and regulators monitor a vast universe of companies with capped budgets, manual workflows, and labels that arrive years after the damage has already spread.
Average audit cost per firm
Manual review consumes high-value expert time, making full-market scrutiny economically unrealistic.
SEC-listed companies to monitor
A massive search space creates a structural mismatch between available personnel and the volume of risk.
Average detection lag
Misstatements often surface late, when reputational, regulatory, and investor losses have already compounded.
Most fraud is hidden, not labeled.
Standard supervised machine learning assumes a clean split between “fraud” and “clean” firms. In financial misstatement detection, the so-called clean set is often contaminated by undetected cases. FinGuard’s Positive-Unlabeled approach treats unlabeled firms as unknowns — not negatives.
Positive-Unlabeled learning
Models confirmed positives while learning from an unlabeled universe that may contain hidden misstatements.
Built for realistic constraints
Robust to detection lag, label noise, and severe class imbalance — the actual data conditions of audit oversight.
Explainable by design
Per-instance interpretability helps reviewers understand why a company is prioritized for further inspection.
A step-change in detection efficiency and capital allocation.
The proposed PU pipeline was validated on a 12-year longitudinal study of SEC filings covering 45,000 cases and 7,000 companies from 2003 to 2014.
More misstatements detected at fixed coverage
At just 10% market coverage, FinGuard’s PU pipeline can identify roughly 34% of misstatements — outperforming the default baseline and existing supervised ML approaches.
| Feature / Metric | Default Baseline | Existing Supervised ML | FinGuard PU Pipeline |
|---|---|---|---|
| Handles unlabeled data | ✕ | ✕ | ✓ |
| Recall @ 10% coverage | ~10% | ~22% | ~34% |
| Cost per detection | $94M+ | $40M+ | $26M+ |
| ROI multiplier | 1.0x | 2.1x | 3.6x |
One risk layer. Three high-value entry points.
FinGuard is designed as a force multiplier for institutions that need to prioritize oversight, diligence, and review under real resource constraints.
Regulators
Catch roughly one-third of misstatements at just 10% market coverage — turning capped budgets into a sharper, data-driven enforcement funnel.
Auditors
Direct expert review to high-risk targets, reducing low-value manual screening and converting sunk audit cost into recovered value.
Investors
Create an early disclosure-risk signal for portfolio screening, lending decisions, underwriting workflows, and governance diligence.
From audit research to deployable financial-risk infrastructure.
The PoC focuses on proving a practical, explainable risk-scoring layer for the 10,000+ company oversight universe — with expansion routes across regulatory technology, audit planning, investor diligence, lending, and insurance underwriting.
SEC-listed companies create a large monitoring universe with constrained review capacity.
coverageTargeted review coverage can become a high-yield queue instead of a broad manual sweep.
focusMisstatement recall at fixed coverage creates a compelling wedge for pilot deployment.
signalA focused path from research foundation to a TRL-6 prototype.
The PoC is structured to move from integrated financial data and model calibration to live scoring, backtesting, and pilot-ready deployment.
Data integration & feature engineering
Build the ingestion layer and feature foundation for SEC filing-based risk scoring.
PU-model calibration & backtesting
Refine the learning pipeline against historical misstatement outcomes and realistic label constraints.
Live risk scoring & pilot deployment
Deliver a review-ready scoring interface for partner evaluation and workflow testing.
TRL 4 → 6 market entry
Advance from validated lab prototype toward relevant-environment demonstration and commercialization readiness.
DICE team of NCSRD
Deep ML research, built for applied financial oversight.
Next steps with FinGuard
We are seeking Proof-of-Concept funding and strategic partners to validate live risk scoring, pilot deployment, and the commercialization path for explainable financial misstatement intelligence.