Transaction Monitoring Service
This project was meticulously designed to monitor transactions received from banking institutions, ensuring high efficiency and scalability. The architecture featured over 100 microservices that seamlessly interacted to manage various banking operations, including exchange rates, core data ingestion mediation, FKG mediation, and background processing. The system was built to handle millions of transactions daily, with a focus on real-time processing and analytics.
Transaction Monitoring
Fintech
Fraud Monitoring
Automation
Performance Optimization
Integration
Client
NicheFinancial TechnologiesLocationUSAYear2024
The client is a leading financial services provider, known for its extensive network of banking institutions and comprehensive financial products. They faced challenges with their existing transaction monitoring system, which struggled with scalability and integration issues. The customer required a modern, scalable solution to improve transaction processing speed, accuracy, and reliability, while also enhancing data security and compliance with financial regulations.Detailed information about the client cannot be disclosed under the provisions of the NDA.
Challenge
The client faced challenges with their existing transaction monitoring system, which struggled with scalability and integration issues. The customer required a modern, scalable solution to improve transaction processing speed, accuracy, and reliability, while also enhancing data security and compliance with financial regulations.
Solution
The solution involved developing a sophisticated system using a microservices architecture to ensure modularity, scalability, and ease of maintenance. Key features of the solution included:
Microservices Architecture
Over 100 microservices were developed using Flask, FastAPI, and Aiohttp, each responsible for specific functionalities such as transaction processing, data mediation, and background tasks.Data Management
MySQL and MariaDB were chosen for their reliability and performance in handling large datasets. These databases were optimized for high-speed transactions and complex queries.Cloud Integration
Azure services like Service Bus, Key Vault, and Container Registry were used for secure and efficient cloud operations. Azure Service Bus facilitated reliable messaging and communication between microservices.Messaging and Observability
RabbitMQ was implemented for robust message queuing, ensuring smooth data flow and process orchestration. Open Telemetry provided comprehensive observability, allowing for real-time monitoring and troubleshooting.Complex Data Relationships
GraphDB was utilized to manage and query complex data relationships, providing insights and analytics that supported decision-making processes.Transaction Monitoring
The system included a transaction monitor that operated based on predefined rules to identify users engaged in excessive transfer activities. This feature helped detect and prevent fraudulent transactions by generating notifications to alert relevant parties when suspicious activities were detected.Results
The implementation of this system led to significant improvements in transaction monitoring and processing efficiency. Key results included:
25%system performance improvement
100 000transactions per minute
2.8seconds of transaction processing time
Enhanced Scalability:The adoption of Kubernetes enabled automatic microservices scaling, ensuring consistent performance even during peak loads, while maintaining a stable requests-per-second (RPS) rate.
Improved Processing Speed:System performance improved by 25%, enabling the handling of over 100,000 transactions per minute without any performance degradation. Average transaction processing time was reduced from 4 to 2.8 seconds through SQL query optimization and the implementation of Redis caching.
OpenTelemetry Integration:Transaction processing time decreased by 20%, from 3.5 to 2.8 seconds, by eliminating bottlenecks such as slow database queries and lengthy external service calls.
Better Data Insights:The integration of GraphDB facilitated advanced data analysis, providing valuable insights into transaction patterns and trends.
Regulatory Compliance:Enhanced data security measures and compliance with financial regulations ensured that the system met all necessary legal and industry standards.
Fraud Prevention:The system's ability to identify and process suspicious transactions effectively prevented fraudulent activities, enhancing overall transaction security.
Team
1
Team Lead
5
Backend devs
3
Frontend devs
1
DevOps
1
Data Engineers
1
Business Analysts
1
Data Analysts
1
QA
Technology Stack:
Backend
- Django REST Framework
- MySQL
- Flask
- FastAPI
- Aiohttp
- SQLAlchemy
- Celery
- Pandas
- Numpy
Frontend
- JavaScript
- TypeScript
- React
- Redux
Platforms
- SA
- AWS
- Lambda
Conclusion
The successful implementation of this advanced transaction monitoring system by Pynest's python developers exemplifies the effectiveness of leveraging a microservices architecture to achieve scalability, reliability, and performance in handling complex financial operations.
Python devs at Pynest contributed significantly to this achievement. The collaborative efforts of a skilled and diverse team, combined with a comprehensive technological stack, resulted in a solution that not only met but exceeded the customer's expectations.This case study highlights the importance of adopting modern technologies and best practices to address the evolving needs of the financial services industry, ensuring robust, secure, and efficient transaction processing in an increasingly digital world. The system's advanced monitoring capabilities and fraud prevention measures have provided the customer with enhanced security and operational efficiency, positioning them for continued success in the competitive financial services market.
