.png)
TrackLight Detect
Leverage AI-powered analytics and social network analysis (TrackLight Connect) to uncover suspicious activity across claims, transactions, payments, and networks – without overhauling your systems or workflows.
Analyze your data. Receive actionable fraud insights instantly.
Proactive Prevention
Stop bad actors before payments are made or programs are compromised. Detect and prevent fraud using our proven fraud scheme library of 3,000+ models.
Behavioral Analytics
We identify patterns of behavior over time, detecting coordinated schemes, unusual activity clusters, and emerging fraud tactics that point-in-time checks can't catch.
Actionable Results
Receive clear, auditable insights: tailored alerts, risk scores, and descriptions of anomalies or program violations. Review and improve results quarterly.
Why TrackLight Detect?
How It Works
Validate people and entities in three simple steps, fast, accurate, and integration-free.
Step 1
Send your program or transactional data — claims, applications, vendor records, or participant lists.
Step 2
We combine rules-based models, machine learning algorithms, network analysis, and artificial intelligence to identify suspicious activity and high-risk entities.
Step 3
Receive a detailed report or dashboard visualizations with risk scores, flagged transactions, and affiliated parties — ready for investigation or compliance action.
Three Core Detection Features
Comprehensive tools to uncover suspicious activity, detect patterns, and prioritize high-risk transactions — all designed to help your team act faster and more effectively.
Transaction Anomaly Detection: Does this activity look normal?
Most claims, transactions, and payment requests follow predictable program rules. Our fraud scheme library flags the ones that don't, identifying anomalies, threshold violations, and suspicious transactions that signal potential fraud.
Fraud Scheme Identification: Does this match a known fraud tactic?
TrackLight's fraud scheme library contains thousands of documented fraud tactics—from simple overbilling patterns to complex multi-entity schemes. We compare your data against known fraud methodologies to identify red flags like phantom billing, identity theft rings, kickback arrangements, shell company networks, and collusion patterns.
Risk Scoring & Explainable Alerts: What should investigators look at first?
TrackLight assigns risk scores to transactions, entities, and claims based on severity and potential financial impact. Every alert includes explainable context: what triggered the flag, which data sources were analyzed, and why it warrants attention.
Common Use Cases:
-
Misrepresentation of benefit eligibility
-
Improper healthcare reimbursements
-
Suspicious grant subrecipient billings
-
Sudden spikes in billing or frequency
-
Claims for services not rendered
-
Identity theft and beneficiary impersonation
-
Provider kickback and referral schemes
-
Shell company and pass-through vendor fraud
-
Collusion between providers and beneficiaries
-
Upcoding and unbundling schemes
-
Medical necessity violations
-
Fictitious employer schemes
Partners
Ready to Detect Fraud Early?
Upload your first dataset or schedule continuous monitoring to gain actionable insights today.