Unsupervised Machine Learning is an artificial intelligence approach that identifies patterns, relationships, and anomalies in data without pre-labeled training examples. This technique is particularly valuable in fraud detection where new patterns emerge constantly.
Key Applications
- Anomaly detection
- Pattern recognition
- Clustering analysis
- Behavior profiling
- Risk assessment
Techniques Used
- Clustering algorithms
- Dimensionality reduction
- Association rules
- Neural networks
- Density estimation
Advantages
- Pattern discovery
- Adaptability
- No labeled data needed
- Real-time learning
- Emerging threat detection
Implementation Areas
- Fraud detection
- Risk management
- Customer segmentation
- Transaction monitoring
- Behavior analysis
Best Practices
- Data preprocessing
- Feature selection
- Model validation
- Regular updates
- Performance monitoring
Unsupervised Learning provides powerful tools for detecting unknown fraud patterns and emerging threats in financial systems.