Discover how a leading provider of financial services for the financial sector unified microservices traceability to detect critical failures in under two hours and significantly optimise operating costs.
The organisation specialises in financing products and payment solutions for large enterprises. It manages business-critical credit and lending operations for millions of customers within a highly complex hybrid technology environment.
Initial situation and context
In financial services, availability is not a luxury. It is a business-critical requirement.
The organisation operates within an advanced technology ecosystem built on microservices and APIs deployed across hybrid and cloud architectures, including GKE and OpenShift. However, despite the robustness of its environment, it faced a common challenge in large-scale infrastructures: fragmented visibility.
Before the project began, the lack of unified monitoring prevented critical incidents from being identified at an early stage. In an environment processing thousands of requests per second, any service degradation — such as a partial outage affecting the lending platform — can escalate rapidly and generate significant financial impact.
What Was the Business Need?
The impact of not knowing represented the organisation’s greatest business risk. A single outage affecting a key service, if not detected in time, could result in financial losses exceeding €500,000 in loan demand.
The organisation needed to address three fundamental priorities:
- Operational independence: Separate its data ingestion architecture from the central corporate environment to increase agility.
- End-to-end traceability: Unify log correlation across requests and responses to eliminate information silos.
- Proactive detection: Move from reacting to customer complaints to automated detection powered by data intelligence.
How Was the Solution Implemented?
To transform the organisation’s operational capabilities, the Datadope team delivered an end-to-end deployment, beginning with the design and implementation of a new log and metrics ingestion architecture. This step was essential to provide the financial services team with greater operational independence.
Once the foundation was in place, we instrumented the microservices to enable the precise collection of real-time performance metrics. The monitoring strategy was further strengthened through the configuration of WebRobot synthetic probes, which simulate real user behaviour, and WebScenario probes, which continuously verify endpoint availability.
All technical data was centralised within Grafana dashboards designed around the Golden KPIs methodology, providing intuitive visibility into latency, saturation, errors and traffic. To shift the operating model from reactive to proactive, we then implemented dynamic Machine Learning-based alerts capable of identifying behavioural anomalies that static thresholds cannot detect.
Finally, we unified log management across third-party service layers, removing information silos and enabling immediate forensic analysis whenever an incident occurred.
Results and Business Benefits
The transformation has turned observability into a form of financial protection for the organisation. Key outcomes include:
- Rapid incident detection: Complex issues can now be isolated and identified in under two hours, preventing costly incidents from continuing unchecked.
- Complete ecosystem control: More than 30 business-critical services are now covered by a fully implemented observability lifecycle.>
- Greater data efficiency: A significant reduction in log noise has delivered direct savings in data processing costs
- A data-driven culture: The team has moved from assumption-based decision-making to managing operations through objective, real-time data.
Today, the organisation does more than monitor its systems. It protects its business and customer experience through an infrastructure that raises the alarm before an issue reaches the bottom line.
