Detection-as-Code for Fraud
An AI agent that authors detection code on your data. No rules to write by hand. No black-box ML. Every detection is readable Python your auditors can verify and your regulators can reproduce.
See it work
The problem
Rule engines require someone to describe every pattern. ML classifiers can't explain their decisions. Manual threat hunting doesn't scale to millions of transactions. When fraud evolves, your defenses are already outdated.
How it works
API gateway, ledger, mobile, webhooks. Any source, one unified view.
The Hunter writes detection code, tests it, and iterates. It finds what you didn't know to look for.
Proven scripts get promoted. The output is readable Python, not a model weight.
Graduated rules run against live streams. Deterministic. Sub-second.
Detection code becomes the STR narrative. Auto-filed to your regulator.
The difference
Other tools give you a risk score. ATROSA gives you the code that found the fraud. Your auditor reads it. Your regulator verifies it. No black box.
{
"risk_score": 0.87,
"reason": "Model confidence"
}
// What does 0.87 mean?if balance_before == balance_after:
if amount > 0:
flag_transaction()
# Your auditor can read this.Proven results
All results from a local model on a single GPU. No cloud API required. Seeded runs accelerate convergence — iteration 93 became iteration 27.
Compliance
Auto-generated SAR/STR reports where the detection logic becomes the narrative. The code that found the fraud explains why it's suspicious. SAR module on the 18-month roadmap; native filing for 70+ jurisdictions at full deployment.
Aligned with CBN Circular BSD/DIR/PUB/LAB/019/002 (Nigeria), SR 11-7 (US Federal Reserve Model Risk Management), and FFIEC BSA/AML Examination Manual. AI-powered anomaly detection, explainable models, automated STR filing, full audit trails.
Early access
ATROSA's autonomous detection platform is in private beta. Design partners get hands-on Shadow Run pilots — 24 hours to first detection on six months of your historical data, with regulator-grade audit artifacts.
Shadow Run pilots run on six months of your historical data and produce regulator-grade audit artifacts within 24 hours. No labels required.