Real-Time Fraud Prevention

Secure transactions across India's diverse financial ecosystem with GramionGroup's unified risk intelligence platform. Block fraudulent transactions in milliseconds.

High Risk Mitigated

Mule Account Detection

Leverage advanced graph neural networks to uncover hidden connections between seemingly unrelated accounts. We track velocity and fund dispersion patterns typical of cash-out operations across IMPS and NEFT networks.

Synthetic Identity Blocking

Correlate device telemetry, behavior anomalies, and public data records to identify synthetic identities constructed from stolen Aadhaar and PAN details during the onboarding phase.

How It Works

Ingest data, run through GramionGroup's core decision engine, and act within 50 milliseconds.

1. Ingestion

Capture transaction payload, device fingerprint, and session biometrics.

2. Evaluation

Query Risk Graph, execute custom rules engine, and run ML fraud models.

3. Decision

Output explicit Allow, Block, or Step-Up (e.g., OTP/Biometric Challenge) response.

Built for enterprise scale

GramionGroup processes billions of events with sub-second latency, ensuring your risk operation never slows down your customer experience.

99.99%
Uptime SLA

Reliability engineered for tier-1 financial institutions.

<50ms
Decision Latency

Real-time risk scoring for instant payment rails.

SOC 2
Type II Certified

Enterprise-grade security and data privacy controls.

Seamless Integration Ecosystem

Connect your existing data warehouses, core banking systems, and identity providers in days, not months. Our API-first architecture ensures GramionGroup works wherever your data lives.

  • Pre-built connectors for major cores
  • Real-time event streaming support
  • Secure batch processing capabilities
  • Customizable webhooks and callbacks
View All Integrations
API Request Payload
POST /v1/risk/evaluate
{
  "event_type": "transaction.created",
  "customer_id": "cust_8f92a",
  "amount": 2500.00,
  "currency": "INR",
  "device_fingerprint": "dev_9x81",
  "ip_address": "103.24.5.11"
}