Everything you need to build robust fraud detection systems. From basic rule-based detection to advanced machine learning algorithms.
Built for scale, performance, and global deployment
Support for 100+ countries with localized fraud patterns and risk profiles
Handle 50+ currencies with automatic USD normalization and conversion
Sub-second fraud detection with high-throughput processing capabilities
Lightweight framework with no external dependencies for easy integration
Comprehensive suite of fraud detection algorithms covering all major attack vectors
Detects unusual transaction frequency patterns
Identifies suspicious transaction amounts
Analyzes geographic fraud patterns
Detects device fingerprinting anomalies
Identifies suspicious IP and network patterns
Analyzes user behavior patterns
Detects time-based fraud patterns
Analyzes merchant-specific risk factors
Machine learning-based fraud detection
Use FraudCatcher in your preferred programming language with consistent APIs
TypeScript support with full type safety
Easy integration with data science tools
Perfect for web applications and APIs
High-performance server-side applications
Get started with just a few lines of code
import { FraudDetector } from 'fraud-catcher';
const detector = new FraudDetector({
rules: ['velocity', 'amount', 'location'],
thresholds: {
velocity: 0.8,
amount: 0.9,
location: 0.7
},
globalThreshold: 0.7
});
const result = await detector.analyze(transaction);
console.log(result.riskScore);from fraud_catcher import FraudDetector
detector = FraudDetector({
'rules': ['velocity', 'amount', 'location'],
'thresholds': {
'velocity': 0.8,
'amount': 0.9,
'location': 0.7
},
'global_threshold': 0.7
})
result = detector.analyze(transaction)
print(result.risk_score)