High-Load Incident Management System in a Bank
TNB technologies
Development
min read
The banking sector is one of the most demanding industries when it comes to reliability and stability of information systems. Every second of downtime - caused by a server crash or an inaccessible API - can lead to financial losses and reputational damage. That is why the development and maintenance of an incident management system becomes a top priority for banks.
Background for Developing the Platform
A major banking organization faced a set of classic yet critical challenges in incident management:
Lack of a unified control point – Information about failures and downtimes was gathered from multiple disconnected monitoring systems. There was no centralized place where real-time data could be viewed.
Delayed response times – Employees were notified about failures with delays or through multiple channels simultaneously, without clear prioritization and role distribution.
Complex coordination – When an incident occurred, multiple teams were involved (DevOps, IT security, infrastructure department), but due to the lack of streamlined processes, coordination was slow and led to overlapping actions.
To address these issues and reduce mean time to recovery (MTTR), the bank initiated a project to create a distributed system capable of:
Monitoring critical services in real time
Structuring all incidents within a unified interface
Key Requirements
High throughput – The system needed to process thousands of events per second from multiple sources (log servers, networks, web services, payment gateways, etc.).
Reliability and fault tolerance – Any failures within the platform itself were unacceptable, as the system was designed to monitor and manage incidents.
Security – Banks handle confidential customer data, and any data breach or unauthorized access could result in severe consequences (fines, loss of trust, reputational damage).
User-friendly interface for different roles – Monitoring operators and technical specialists required different access levels and toolsets to facilitate teamwork and accelerate decision-making.
Architecture and Implementation
The project was built on a microservices-based architecture, ensuring scalability and distributed data processing. Each functional area (monitoring, notifications, event correlation, analytics) was deployed as an independent service with its own API. This approach allowed individual components to be updated and scaled separately, reducing the risk of single points of failure.
Key Components:
Event Collection – Agents installed on the bank’s servers and services track logs and system metrics (CPU usage, memory, network traffic) and send data to a central message broker.
Processing & Correlation – After reaching the message broker, data is distributed to dedicated microservices. Machine learning algorithms analyze logs, compare real-time metrics with historical data, and detect anomalies or potential incidents.
Incident Management – When a failure is detected, an incident "card" is automatically created, specifying priority, status, responsible teams, and other details. All user actions are logged, creating a traceable history.
Notifications & Escalation – The system is integrated with corporate messengers and email systems. Alerts are sent to responsible personnel, with the format and channel (SMS, Push, email, corporate messenger) depending on the incident’s severity.
Reporting & Analytics – Executives and department heads have access to visual dashboards displaying key metrics such as downtime duration, transaction success rate, and average incident resolution time.
Security & Load Distribution
The system was deployed across multiple geographic locations within the bank’s data centers, with redundancy for key components. If one data center failed, another would automatically take over the workload, ensuring zero downtime.
Data is stored in encrypted format
Multi-factor authentication (MFA) and role-based access control (RBAC) regulate system access
Results & Impact
Reduced MTTR by 40%
Incident detection and resolution times were significantly reduced, as employees could view all incidents in a single interface and receive clear action instructions.
Increased Process Transparency
With the logging and tracking system, managers could see how resources were allocated and which teams were responsible for specific tasks.
Elimination of Recurring Issues
Correlation tools helped identify patterns and address root causes, rather than just fixing symptoms.
Optimized Load Management
By analyzing system workload, the bank could proactively scale critical services before peak periods (e.g., financial reporting deadlines or high transaction volumes).
Strategic Benefits for the Bank
Enhanced Customer Trust – Service uptime is a key factor for customers choosing a bank. Even short-term unavailability of mobile or internet banking can result in customer churn.
Reduced Financial Losses – Faster incident resolution minimized risks related to delayed payments or failed transactions.
Stronger Reputation Among Partners – A transparent incident resolution system increased the bank’s credibility with business partners and regulators.
Conclusion
The development of a high-load distributed incident management system successfully addressed the challenge of monitoring critical services and rapidly responding to failures.
The project demonstrated that a comprehensive approach to system development, deployment, and employee training can significantly reduce downtime while improving the overall maturity of the bank’s IT infrastructure.
