Financial Services
1 Full-stack Developer, 1 ML Engineer, 1 PM
Three months (Ongoing)
We collaborated with a client in the FinTech industry to enhance their data analytics platform. The platform is designed to validate data from multiple sources and generate quality reports, helping users assess the reliability and accuracy of their datasets. The platform’s primary users are financial organizations, including banks, that rely on precise data quality checks to make critical business decisions.
The client’s version of the platform could no longer keep pace with their increasing demands, leading to inefficiencies in data validation and reporting processes. Challenges with scalability, reliability, and inconsistent data quality have undermined end-user trust and satisfaction. Outdated architecture and insufficient testing resources left the system vulnerable to critical reporting defects. There was an urgent need for a faster, cost-effective solution to ensure smooth client operations.
To address the client’s needs and unlock the platform’s full potential, our work focused on delivering targeted solutions with following improvements:
The initial system’s outdated and inefficient architecture hindered performance and scalability. Through targeted refactoring, we optimized processes and enhanced platform efficiency. The new solution retains core functionalities while streamlining workflows, offering clients a more reliable and cost-effective experience
We developed comprehensive unit and E2E Playwright tests to enhance platform stability and preemptively address defects
To ensure higher accuracy, faster data processing, and improved data quality for end-users, we implemented an ML model designed to structure and format data in a way that is optimal for further analysis and visualization
Interactive charts, graphs, and infographics, to present complex information in a clear and engaging manner. These visuals are designed to help users quickly identify and understand key trends, outliers, and performance metrics. Users could manipulate visualizations, filter data, and explore different scenarios
Using DevOps pipelines, we simplified the process of aggregating and deploying data for clients. The updated system enables releasing features individually for each customer, providing flexibility and a customized experience tailored to their specific needs
The refactoring of the platform’s architecture led to significant improvements in both performance and scalability. By optimizing workflows and streamlining backend processes, the new platform is faster and more efficient, allowing financial organizations to handle larger volumes of data with greater ease.
Besides, we:
Up to 50% fewer critical system errors.
Comprehensive unit and E2E Playwright tests ensured that critical defects were identified and resolved early in the development process, resulting in fewer errors and improved system stability
The transition to a more modular and scalable infrastructure now enables the platform to offer more tailored solutions to clients
Simplified architecture and optimized data processing reduced the overall operational costs
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