Detect Financial Fraud Before It Hits Your Portfolio
Six institutional-grade financial models in a single API call. Beneish M-Score, Altman Z-Score, Piotroski F-Score, and more — from one REST endpoint.
See It In Action
Enter any stock ticker to get a comprehensive financial risk analysis powered by established academic models.
Apple Inc.AAPL
Technology · 2026-02-21
Apple Inc. shows strong financial health with low manipulation risk and solid fundamentals.
Red Flags (2)
How It Works
From ticker to intelligence in under 200ms.
Send Ticker
Make a single API call with any stock ticker symbol.
GET /v1/scan/AAPLWe Crunch Numbers
Six financial models analyze income statements, balance sheets, and cash flows.
6 models x 5 years dataGet Intelligence
Receive structured risk assessment with scores, flags, and plain-language explanations.
{ compositeRisk: 22 }Institutional-Grade Financial Models
Built on peer-reviewed academic research, each model targets a different dimension of financial risk.
Beneish M-Score
Detects earnings manipulation using 8 financial ratios
> -2.22 = likely manipulatorBeneish, M.D. (1999) — Financial Analysts Journal
Altman Z-Score
Predicts bankruptcy probability within 2 years
< 1.81 distress | > 2.99 safeAltman, E.I. (1968) — Journal of Finance
Piotroski F-Score
Evaluates financial strength on 9 binary criteria
0-9 score (9 = strongest)Piotroski, J.D. (2000) — Journal of Accounting Research
Accrual Quality
Measures earnings quality via cash flow analysis
OCF / Net Income < 1.0 = poorSloan, R.G. (1996) — The Accounting Review
Composite Risk Score
Weighted combination of all models into 0-100 score
0 = safe | 100 = highest riskProprietary — FinScan methodology
One Endpoint. Complete Analysis.
A single REST call returns six financial models with plain-language explanations.
curl -H "X-API-Key: YOUR_KEY" \
https://api.finscan.dev/v1/scan/AAPL{
"ticker": "AAPL",
"companyName": "Apple Inc.",
"sector": "Technology",
"compositeRisk": {
"score": 22,
"riskLevel": "LOW",
"summary": "Low overall financial risk"
},
"beneishScore": {
"mScore": -2.87,
"interpretation": "Unlikely manipulator",
"isLikelyManipulator": false
},
"altmanScore": {
"zScore": 4.12,
"zone": "SAFE",
"interpretation": "Low bankruptcy risk"
},
"piotroskiScore": {
"fScore": 7,
"maxScore": 9,
"interpretation": "Strong financial health"
}
}Supporting 10,000+ tickers across NYSE, NASDAQ, and global exchanges
Simple, Transparent Pricing
Start free, scale as you grow. All plans include access to every financial model.
Free
10 scans per month
- 10 scans per month
- All 6 financial models
- JSON API response
- Community support
Starter
100 scans per month
- 100 scans per month
- All 6 financial models
- JSON API response
- Email support
- Batch scanning
Professional
500 scans per month
- 500 scans per month
- All 6 financial models
- JSON API response
- Priority support
- Batch scanning
- Webhook notifications
Enterprise
Unlimited scans
- Unlimited scans
- All 6 financial models
- JSON API response
- Dedicated support
- Batch scanning
- Webhook notifications
- Custom integrations
Built on Peer-Reviewed Research
Beneish, M.D. (1999)
“The Detection of Earnings Manipulation.” Financial Analysts Journal
Altman, E.I. (1968)
“Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy.” Journal of Finance
Piotroski, J.D. (2000)
“Value Investing: The Use of Historical Financial Statement Information.” Journal of Accounting Research
Sloan, R.G. (1996)
“Do Stock Prices Fully Reflect Information in Accruals and Cash Flows?.” The Accounting Review