💡 Competitive Analysis

How We're Different

Most payment risk tools use static rules from 5 years ago. We use ML models trained on 70,000+ scenarios that adapt to real PSP behavior.

MerchantGuard vs Traditional Risk Tools

Training Data

⚡ MerchantGuard
70,000+ profiles
Traditional
0 samples

Risk Assessment

⚡ MerchantGuard
ML-powered predictions
Adapts to PSP behavior patterns
Traditional
Static if/then rules
Fixed logic from 2019

PSP Coverage

⚡ MerchantGuard
7 PSP-specific models
Tuned to each processor
Traditional
One-size-fits-all
Generic scoring

Model Validation

⚡ MerchantGuard
Holdout test sets
70-87% accuracy, ROC-AUC 0.74-0.91
Traditional
Untested
No validation metrics

Updates

⚡ MerchantGuard
Continuous retraining
Learns from new merchant data
Traditional
Manual updates
Rules updated annually

Free Tier

⚡ MerchantGuard
✓ Yes
Traditional
✗ No

Why This Matters

📊

Real Data Beats Guesswork

Our models learned from 70,000+ scenarios. Traditional tools rely on outdated rules from 2019 that don't reflect current PSP behavior.

🎯

PSP-Specific Predictions

Stripe has different approval patterns than Checkout.com. Our 7 separate models capture these nuances—competitors use generic scoring.

🔄

Continuous Improvement

We retrain models with new merchant data quarterly. Rule-based tools stay stuck with the same logic for years.

Try the ML-Powered Approach

Get your free AgentScore in 60 seconds. See PSP approval odds based on 70,000+ training scenarios—not outdated rules.

Get Free Assessment →Learn About Our AI

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📊 Transparency Note

Our ML models are trained on synthetic merchant and agent data generated from industry research and PSP documentation. Models validated using holdout test sets (70-87% accuracy, ROC-AUC 0.74-0.91). Real-world performance may vary. For educational purposes.