Telegram Antifraud Analytics for Media Plans
Fraud detection system reduces inefficient spending by 24% and automates verification of 100 channels in 12 minutes
One-liner: Reduced inefficient spending by 24% and converted audit of 100 channels to 12 min (p95) mode at Precision@Fraud 0.90.
Key metrics: −24% inefficient spending • 12 min/100 channels (p95) • Precision@Fraud 0.90 • Recall@Fraud 0.70 • FP rate 9% • FraudScore 0–100
Self-learning SLO: feedback recalibration p95 < 5 min, explainability coverage 100%, drift alert < 5 min.
Probability calibration: control Brier and ECE for P/R stability.
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FAQ
What does this antifraud system detect in practice?
It detects suspicious channel behavior and spending anomalies using a hybrid of rule-based checks and statistical anomaly detection.
How does the system stay reliable over time?
Adaptive thresholds, feedback-based recalibration, and drift alerts keep detection quality stable as channel behavior and campaign patterns evolve.
What is the operational benefit for media teams?
Verification that previously took manual analyst hours is compressed into a predictable batch workflow with measurable precision and response-time targets.
