Case Study: Pagantis achieves real-time, scalable fraud detection and risk scoring with TigerGraph

Improving Performance and Scale for Critical Risk-Scoring and Anti-Fraud Processes

Pagantis, a consumer finance provider for e-commerce, needed near real-time credit scoring and anti-fraud analysis to speed customer onboarding but found its relational database could not handle the low-latency, relationship-driven analytics required. After evaluating alternatives, Pagantis chose TigerGraph’s native parallel graph database (deployed on AWS) to support its risk-scoring and fraud-detection workflows.

Using TigerGraph, Pagantis pre-connected customer data and ran built-in parallel graph computations and real-time updates to deliver fast, relationship-based insights. TigerGraph enabled significantly reduced user wait times, accelerated anti-fraud and risk processes, attracted more new business, and provided a scalable foundation to deliver the service at scale.

Pagantis

Martynas Sukys

Product Owner