Case Study
Finance

Invoice Reporting for International Bank

Modernize legacy systems by using big data pipelines, spark applications and microservices to drive visualizations and billing reports for millions of events.

Invoice Reporting

Project Overview

Through Spark-powered data pipelines, microservices-driven workflows, and intuitive visualization tools, we helped an international bank modernize its invoice reporting system to cope with massive transaction volumes. The result is a future-proof, highly scalable solution that streamlines processes, reduces errors, and delivers real-time financial insights. This strategic overhaul underscores how embracing big data technologies and modular service architectures can revolutionize legacy systems, ensuring both agility and reliability in today’s fast-paced, regulated financial landscape.

Complex Data Queries from Legacy Systems
Extracting billing and transactional information from legacy systems often involves cumbersome queries and fragmented data sources. These systems might have limited API support or lack modern integration points, slowing down the retrieval process. By implementing standardized data extraction workflows and mapping legacy fields into more flexible formats, organizations can reduce complexity and ensure more consistent reporting. Frequent data validation checks also help identify gaps or anomalies early. In doing so, businesses turn cumbersome queries into optimized, repeatable processes, transforming historical systems into manageable components that feed into modern analytics and invoicing solutions.
Leveraging a Big Data Hadoop Platform
A Hadoop-powered platform provides the scalability and fault tolerance necessary to process vast amounts of raw data efficiently. Batch processing engines and distributed file systems within the Hadoop ecosystem can handle petabytes of transactions, offering high-speed aggregation and transformation capabilities. This data lake approach centralizes disparate data sources—ranging from mainframe logs to real-time event streams—enabling more advanced analytics on invoice and billing data. By layering services such as Spark on top of Hadoop, organizations gain near real-time querying and predictive modeling features. The result is a robust, cost-effective infrastructure that forms the backbone of modern invoice reporting.
Automating Invoice PDF Creation
Once data is cleaned, aggregated, and validated, automating the generation of invoice PDFs becomes a critical step. By integrating with templating engines and leveraging standardized data formats, organizations can produce consistent, branded invoices that adhere to regional and compliance requirements. The automation process can embed important metadata—such as invoice numbers, payment due dates, and customer details—directly into the PDF files. Furthermore, automated workflows handle large-scale batch processing, ensuring that invoices are generated quickly without manual intervention. This approach minimizes errors, saves staff time, and promotes timely distribution to clients and internal stakeholders.
Client Self-Service with Portal Access
Allowing clients to view and download their invoices through a secure web portal greatly enhances transparency and satisfaction. Customers can log in at their convenience, review billing history, and retrieve PDFs of current or past invoices. This self-service model reduces inbound support requests and accelerates dispute resolution, as clients have ready access to relevant details. Built-in notifications and alerts can keep stakeholders updated about payment statuses or upcoming due dates. By offering robust authentication and encryption, the portal ensures sensitive financial data remains protected, reinforcing trust and fostering long-term relationships with clients.

Challenge

An international bank needed to upgrade its outdated invoice reporting systems, which struggled to handle an ever-increasing volume of transactions and billing events across multiple regions. The legacy infrastructure limited real-time visibility into financial data and required significant manual intervention to reconcile invoices. Scaling issues, slow reporting cycles, and fragmented data sources further compounded the challenge, making it difficult to accurately measure revenue streams and comply with regulatory requirements. Decision-makers required a modern solution that could efficiently process millions of events, deliver clear visual insights, and ensure accuracy and compliance across global operations.

Solution

The modernization effort revolved around designing a robust big data pipeline, incorporating Apache Spark for high-speed data processing and microservices for modular, scalable capabilities. Transactions and billing events were ingested in real time, then enriched and aggregated in Spark-based workflows that fed into a centralized data lake. A set of lightweight microservices managed tasks such as invoice generation, currency conversions, and compliance checks, all orchestrated through containerization tools. Finally, an intuitive dashboard solution integrated seamlessly with the pipeline, allowing stakeholders to visualize billing performance, track revenue, and make adjustments on a near real-time basis.

Results

By replacing the legacy system with a cloud-native, data-driven architecture, the bank gained the ability to process and analyze millions of transactions rapidly. Invoice accuracy improved significantly, reducing manual interventions and errors. The new dashboards offered clear and timely visibility into key financial metrics, enabling faster decision-making and better compliance reporting. Overall, this transformation enhanced operational efficiency, shortened billing cycles, and provided the scalability needed to support the bank’s expanding customer base. In turn, the reliable, on-demand access to critical invoice and revenue data elevated customer service and positioned the bank for continued growth and innovation.

Technologies Used

Angular
Java
Oracle
Atlassian Suite
Impala
Hive
Power BI
Tableau
Kubernetes
Kafka

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