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.
