Fraud scoring built
for Indonesian
payment patterns..
A real-time fraud detection system for QRIS, e-wallet, card, bank transfer, and social-engineering patterns in Indonesia.
A fraud system that doesn't know your market
isn't protecting it.
Generic fraud models often miss local tactics, block legitimate customers, and create alert queues that are hard for analysts and compliance teams to trust.
False positive
QRIS chains, e-wallet movement, mule behavior, and WhatsApp scam flows need Indonesia-specific signals.
Local pattern gaps
QRIS payment chains, e-wallet transfers, and local WhatsApp scams are invisible to models built for other markets.
Risk decisions
Black-box scores make it difficult for compliance teams to explain why a transaction was blocked or escalated.
Customer friction
Unclear fraud rules can interrupt good customers and weaken trust in the transaction experience.
Five steps.
Four run automatically..
Your fraud team sets risk thresholds, reviews escalations, and approves policy changes. The system scores, explains, routes, and learns from analyst feedback.
Every score comes with a reason. Your compliance team can review & defend
What your team actually sees.
LLive dashboard, risk scores, flagged transactions, account links, analyst actions, and plain-language explanations in one view.
Less alert noise.
More useful fraud signals.
.
-
Fewer false positives
Analysts spend more time on meaningful risk and less time clearing noise.
-
Live transaction scoring
Risk decisions can happen inside the transaction flow without relying only on delayed review.
-
Explainable decisions
Every score includes a clear reason for analyst, customer, and compliance review.
-
Adaptive detection
The model improves as fraud tactics change and analysts provide feedback.
Show us your false positives.
We'll show you what local scoring catches.
We re-score a sample of your past transactions, compare it against your current workflow, and show where local fraud signals improve detection and reduce noise.
Test before live deployment
We review sample transactions and show false alerts, missed patterns, and explainability gaps without changing your live systems.