FraudShield AI — Real-Time Financial Fraud Detection

🛡️ FraudShield AI — Catching Fraud Before It Happens

Inspiration

Financial fraud costs the global economy $5.8 trillion annually. Most detection systems either miss fraud or block legitimate transactions with high false-positive rates. I wanted to build something that thinks like a fraud analyst — fast, explainable, and accurate.

What It Does

FraudShield AI is a real-time transaction risk scoring system that analyzes every transaction across 12+ behavioral and contextual signals:

💰 Transaction amount anomaly detection
🌍 Geographic risk profiling (country + geo-velocity)
⏰ Time-of-day behavioral analysis
📱 Device fingerprint validation
🏪 Merchant category risk scoring
👤 Account age & transaction history patterns

Every analysis produces an explainable output — not just a score, but the exact factors that triggered the alert. This is Explainable AI (XAI) in practice.

How I Built It

Risk Engine: Multi-signal weighted scoring algorithm inspired by Isolation Forest anomaly detection principles
Frontend: Vanilla JavaScript with real-time UI updates
Visualization: Chart.js for live risk history tracking
UX: Three pre-built demo scenarios (Legit / Suspicious / Fraud) for instant testing

Challenges

The biggest challenge was calibrating the risk weights so the system catches real fraud without flagging normal transactions. I iterated on the scoring logic using real-world fraud pattern datasets from published research (IEEE-CIS Fraud Detection dataset patterns).

What I Learned

Building explainable AI is harder than building accurate AI. It's not enough to say "this is fraud" — the system must tell you why, so analysts can act on it.