Transform Fintech Risk Management With DevOps Automation
Overview
A FinTech firm dealing with millions of daily transactions needed an automated incident response system to minimize downtime, enhance customer experience, and improve service reliability. By implementing robust DevOps practices, we enabled faster recovery, proactive monitoring, and real-time issue resolution across their complex, microservices-based infrastructure, ensuring seamless operations at scale.
Technical Stack
Industry
Finance
Region
United States
Project Size
Non- Disclosable
Highlights
Reduced MTTR by 70%
Achieved 99.99% system uptime
Increased incident resolution speed by 85%
40% lower operational costs
Challenges & Solutions
It impacted service reliability and caused extended downtimes that frustrated customers.
- Solution: We implemented automated monitoring with Prometheus and Grafana. This allowed the system to detect issues instantly and trigger automated recovery processes, significantly reducing MTTR by 70% and ensuring rapid issue detection and response.
Manual Incident Management Slows down Resolution and delayed response times and reduced overall service efficiency.
- Solution: Using AWS Lambda and Jenkins CI/CD, our team deployed an automated incident response framework with real-time alerts. We automated response processes for recurring issues, increasing resolution speed by 85%.
The complexity of the microservices setup created vulnerabilities in uptime and service continuity.
- Solution: We leveraged Kubernetes to orchestrate and manage microservices across multiple nodes. This provided redundancy, enabling seamless failover and auto-scaling, achieving 99.99% uptime and seamless failover for uninterrupted service.
High Operational Costs Due to Inefficient Resource Allocation: Inefficient resource allocation drove up operational expenses, with resources either overused or underutilized, leading to unnecessary costs without optimizing system performance.
- Solution: We optimized compute resources by implementing resource allocation policies based on actual demand patterns. Auto-scaling on Kubernetes enabled dynamic resource allocation, reducing costs by 40% and optimizing performance during peak loads.
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Core Features
- Automated incident response with AWS Lambda
- Continuous integration and delivery with Jenkins
- Real-time monitoring with Prometheus and Grafana
- Redundant Kubernetes clusters for high availability
- Dynamic auto-scaling to optimize resources
- Proactive issue detection and alerting
- 24/7 support and incident management
- Cost-effective resource allocation
No. of Developers
06
Time Frame
March 2023 - Ongoing
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