Trading Transaction Post-Processing Service
- The project aimed to enhance post-trading transaction operations by decomposing transactions into separate entities for specific analytics and custom reporting. It seamlessly integrated with multiple third-party platforms for bidirectional synchronization of transaction data.
Client
The company is a leading financial institution specializing in investment banking. The client required a robust solution to optimize their post-trading transaction processes and improve analytical capabilities.
Detailed information about the client cannot be disclosed under the provisions of the NDA.
Solution
The project leveraged a distributed monolith architecture to manage the complexity of post-trading transaction processing. The primary business logic was encapsulated within a PostgreSQL database using stored procedures and views. These database artifacts were automatically exposed as a GraphQL API through Hasura, facilitating efficient data access and manipulation. Complementing this architecture, a suite of over 15 microservices was deployed to handle specialized tasks and asynchronous processing that were challenging to execute within the constraints of a relational database alone.
The integration with third-party platforms was crucial for seamless transaction synchronization. APIs were developed to ensure bidirectional data flow with seven key third-party systems involved in various post-trading operations. This integration allowed for real-time updates of transaction statuses and facilitated timely reconciliation, reducing the risk of discrepancies and enhancing operational transparency.
Results
The implementation of the solution yielded significant improvements in operational efficiency and analytical capabilities:
25%
increasing overall throughput
40%
improvement of decision-making speed
50%
decrease of manual intervention
Operational Efficiency:
Transaction management time was reduced from hours or days to minutes, increasing overall throughput by 25%.
Analytics and Reporting:
Granular insights and swift custom report generation improved decision-making speed by 40%.
Improved Payment Processing:
Real-time synchronization with third-party platforms decreased manual intervention by 50% and enhanced operational agility.
Team
1
Team Lead
4
Backend devs
3
Frontend devs
1
DevOps
1
Business Analysts
1
QA
Technical Stack:
Backend
Cloud Services
Containerization and Orchestration
Conclusion
The collaboration between the Pynest team and the client's domain experts ensured the project's success in meeting the specific requirements of the financial services industry. Proficiency among Pynest’s developers, combined with a thorough understanding of financial transaction processing and integration challenges, enabled the creation of a robust, scalable, and efficient solution.
With the support of Pynest's Python developers, we are dedicated to continuing to maintain and evolve the implemented solution. Drawing on our expertise, we aim to enhance performance, introduce new features, and adjust to evolving regulatory and market dynamics. This commitment highlights our focus on delivering customized Python solutions that foster operational excellence and drive business growth for our clients.