A global financial institution was losing over 50% of trading requests due to a slow, manual equities platform. By modernizing its system on AWS using microservices, the bank reduced processing time by up to 90% and enabled near real-time responses. This transformation removed a critical barrier to AI adoption and positioned the business for scalable growth.
Why was the financial institution losing trades?
The institution relied on a legacy, on-premises platform to process equity trades. The system depended on manual calibration of market data, which slowed everything down. Traders had only a short window to respond to customer requests. In many cases, they simply ran out of time. As a result, more than half of all requests were rejected. If traders can’t respond in time, the opportunity is already lost.
Why couldn’t the system support real-time or AI-driven decisions?
The issue was how the system was designed.
- Data was fragmented across systems with limited visibility
- Processes relied on manual intervention
- Core components were tightly coupled, making change slow and risky
For financial institutions, this creates a broader problem:
- AI models cannot access reliable, real-time data
- Systems cannot support real-time risk, pricing, or trading decisions
- Integration with modern platforms (cloud, APIs, AI) becomes difficult
This made it difficult to scale or respond quickly. It also meant the bank couldn’t use AI. Real-time analytics and automation require fast, accessible data. The legacy system couldn’t support that. Modernization was the prerequisite for real-time and AI.
How did the financial institution modernize its trading platform?
The company moved its equities platform from an internal data center to AWS and redesigned how it worked. Instead of one large system, it adopted a microservices architecture. This split the platform into smaller, independent services that could update and scale on their own. To support this, the bank implemented:
- Amazon EKS (Kubernetes): runs applications in flexible environments that scale during trading peaks
- Amazon Aurora: provides faster access to market data
- Amazon MSK (Kafka): enables real-time communication between system components
This new design allowed the platform to handle demand dynamically and respond faster to market changes.
Why did the bank choose SoftServe?
Because this was the institution’s first major cloud modernization initiative, it needed a partner who could operate in a financial services environment with strict performance, security, and compliance requirements.
SoftServe was selected for its ability to:
- Design scalable, low-latency architectures for mission-critical systems
- Ensure security, compliance, and operational resilience
- Establish a repeatable framework for future modernization across core platforms
What changed after modernization?
The bank moved from reactive processing to real-time execution, and the system is now ready for AI-driven trading insights.
- Calibration time dropped by up to 90%
- Traders could respond in near real time
- The platform scaled with demand instead of slowing down
What this means for AI
Before modernization, the bank couldn’t realistically use AI. After modernization, it now has:
- Real-time data
- Scalable infrastructure
- Modular architecture
