Real-Time Fraud Detection for Fintech
Move fraud signals, behavioral data, and risk scores in under 100ms, so decisions happen before transactions settle, not after losses hit your P&L.
Key Benefits
Faster Time-to-Market
Ship in weeks, not quarters.
Benefit
Avoid months of building and hardening custom WebSocket or streaming infrastructure. Focus engineering on models, rules, and fraud strategy instead of plumbing.
Live Fraud Ops Visibility
Real-time queues, not polling dashboards.
Benefit
Push high-priority cases and anomaly alerts to fraud ops tools instantly so teams see emerging attacks in real time and coordinate response earlier.
Predictable Cost & Lower TCO
Forecastable economics as volume grows.
Benefit
Replace DIY infra spend, on-call burden, and compliance overhead with a managed platform whose cost scales with clear usage drivers you can model and govern.
Batch Fraud Detection Can’t Keep Up With Machine-Speed Attacks
Fraud decisions arrive after settlement while attackers operate in milliseconds. Losses, chargebacks, and regulatory exposure pile up before teams even see the pattern.
Static rules and batch pipelines can’t correlate device, behavioral, and transaction signals quickly enough, forcing you to choose between blocking good customers or absorbing fraud.
You need a real-time event fabric that feeds risk engines and fraud ops instantly without another fragile infrastructure project on your roadmap.
Why Fintech Teams Choose PubNub for Fraud Detection
Compare building on generic streaming tools or point solutions with adopting a managed real-time event fabric designed for risk-critical workloads.
PubNub vs. DIY on Kafka & WebSockets
PubNub removes months of building, scaling, and securing edge connectivity while Kafka stays focused on ingestion and analytics.
PubNub vs. Point Solution Patchwork
Replace multiple device, push, and WebSocket vendors with one managed fabric for signals, alerts, and behavioral streaming.
CASE STUDY
How Fintech Teams Use PubNub for Real-Time Fraud Detection
Fintech platforms stream transaction events, behavioral biometrics, and risk scores through PubNub to make decisions before authorization, feed live fraud ops queues, and maintain audit-ready histories—without diverting core engineering from product roadmap commitments.
Get an ROI & Architecture Review for Fraud Detection
Frequently Asked Questions
How does PubNub support real-time fraud detection?
PubNub provides a fully managed real-time event fabric. Transaction events, behavioral signals, and risk scores are streamed between microservices, risk engines, and dashboards in under 100ms, enabling pre-authorization fraud decisions and instant alerting when anomalies are detected.
Where does PubNub fit alongside Kafka and my data platform?
Kafka and data platforms are excellent for ingestion and analytics. PubNub focuses on the last-mile, interactive path: moving fraud signals between client apps, APIs, decision engines, and operations tools in real time without you operating edge connectivity or WebSocket infrastructure.
Can PubNub help with PSD3, DORA, and audit requirements?
PubNub offers message persistence, delivery receipts, and granular metadata that you can use as part of your audit trail for incident classification and reporting. This reduces the amount of custom logging and monitoring infrastructure you need to build to support PSD3 and DORA obligations.
How does PubNub protect sensitive financial and identity data?
Architecturally, most customers send signal metadata and references rather than raw PII or full transaction payloads. PubNub supports encryption, fine-grained access control, and isolation so you can align data flows with your internal security and privacy standards.
What does implementation look like for an existing fintech stack?
Teams typically start by streaming a narrow set of transaction events and risk decisions through PubNub for one use case (for example, high-value payment review). From there, they extend to additional signals, dashboards, and user-facing alerts as value is proven.
How do we manage cost and avoid unexpected usage spikes?
Because pricing is tied to clear usage drivers, you can forecast based on expected active users, transaction volume, and fraud signal patterns. Monitoring and configuration guardrails help you govern traffic and keep spend aligned with business value.
