Detect fraud in real time before it impacts your customers
AI agents that continuously monitor transactions, identify anomalous patterns, and trigger instant alerts — reducing false positives by up to 60% while catching more real threats.
Built for Fraud Analysts, Risk Officers & Compliance Teams
The Problem
Why manual triage doesn't scale
Rule-Based Systems Miss Sophisticated Fraud
Legacy fraud detection relies on static rules that cannot adapt to evolving attack vectors, letting sophisticated fraud slip through while flagging legitimate transactions.
Traditional systems miss up to 40% of complex fraud schemes
Too Many False Positives
Analysts waste hours reviewing false alerts, creating investigation fatigue and allowing real fraud to go unnoticed in the noise.
Slow Investigation Turnaround
Manual fraud investigation processes take days to resolve, leaving customers exposed and increasing chargeback costs.
Average fraud case resolution: 3-5 business days
Siloed Data Across Systems
Transaction data, customer profiles, and behavioral signals live in separate systems, making it impossible to see the full picture.
Results
Measurable impact from day one
60%
Fewer False Positives
AI dramatically reduces false alerts so analysts can focus on real threats.
95%
Detection Accuracy
ML models catch fraud with near-perfect accuracy across transaction types.
80%
Faster Case Resolution
Automated enrichment and orchestration slash investigation turnaround times.
3x
More Fraud Caught
Adaptive models detect significantly more real fraud than rule-based systems.
Capabilities
Everything you need for intelligent triage
Real-Time Transaction Monitoring
AI agents analyze every transaction in milliseconds, comparing against behavioral models, device fingerprints, and historical patterns.
- Anomaly detection across multiple dimensions
- Behavioral biometrics integration
- Cross-channel correlation
Adaptive ML Models
Self-learning models that continuously evolve with new fraud patterns, reducing manual rule updates and staying ahead of emerging threats.
- Automated model retraining
- Feedback loop from analyst decisions
- Zero-day fraud pattern detection
Automated Case Orchestration
Intelligent routing of alerts to the right analyst with pre-enriched context, priority scoring, and recommended actions.
- Risk-based alert prioritization
- Automated evidence gathering
- One-click case escalation
Regulatory Compliance
Built-in compliance with PCI DSS, PSD2, and anti-money laundering regulations with full audit trails.
- SAR filing automation
- Regulatory reporting
- Complete audit trail
How It Works
Three steps to automated triage
Step 1
Ingest & Analyze
Ingest transaction streams, enrich with customer and device data, and run real-time pattern analysis.
Step 2
Detect & Score
ML models score every transaction for fraud risk, flagging suspicious activity with confidence levels.
Step 3
Alert & Resolve
Route alerts to analysts with full context, automate case workflows, and feed outcomes back to improve models.
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