Case Study: Wirecard AG achieves real-time, scalable transaction monitoring and faster error response with Elastic

How Wirecard uses the Elastic Stack to monitor transactions & analyze errors

Wirecard AG, a global provider of outsourcing and white‑label electronic payment solutions, needed to ensure fast, highly available processing of card transactions across many distributed systems. Their prior monitoring relied on frequent SQL queries against a central database, which did not scale as transaction volume grew and forced a trade‑off between monitoring frequency and system load, delaying anomaly detection and troubleshooting.

Wirecard replaced database polling with the Elastic Stack—using Metricbeat, Packetbeat, Filebeat, Logstash, Elasticsearch, Kibana and X‑Pack—streaming Layer‑7 logs from F5 BIG‑IP load balancers into two Logstash instances and a three‑node Elasticsearch cluster. The new setup provides real‑time dashboards and five‑second alerts for acceptance/rejection rates, card‑scheme and data‑center distribution, error codes and transaction percentiles, with no measurable load on processing systems; response times and root‑cause analysis improved significantly, and Wirecard plans to expand logging and evaluate X‑Pack machine learning.

Wirecard AG

Jan Krynojewski

Head of Service Delivery Acquiring Processing