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

Perform a project

Client

Location

USA

Niche

Financial Technologies

Year

2024

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.

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

100 000

transactions per minute

2.8

seconds 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

Technology stack

backend

Django REST Framework

MySQL

Flask

FastAPI

Aiohttp

SQLAlchemy

Celery

Pandas

Numpy

Frontend

JavaScript

TypeScript

React

Redux

Platforms

SQS

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.