Optimizing Data Analytics and Backend Processes for a Financial Institution

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Finance

Optimizing Data Analytics and Backend Processes for a Financial Institution

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Focus Areas

IT Infrastructure

Digital Transformation

Data Analytics

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Business Problem

A large financial institution group was facing problems with generating timely financial reports. The group used Elasticsearch databases and MySQL to extract data and generate financial reports. However, this report generation process was slow and prone to errors, and delayed the delivery of accurate financial insights to the internal leadership team as well as external stakeholders such as regulators and investors.

Key challenges:

  • Slow Report processing: The client’s existing system – reliant on MySQL and ElasticSearch databases – took too long to extract data to generate reports. The delay frustrated external stakeholders who needed these insights to make timely financial decisions and also slowed down internal decision-makers.
  • Data extraction prone to error: The reports generated from the existing system were prone to errors due to the inefficiencies of the data extraction process. This often led to the need for manual corrections which caused further delays. 
  • Out-of-date backend infrastructure: While the institution and the volume of data had been scaling, the backend system architecture had not been. Due to this, as more data flowed in, the performance of the backend system reduced, and led to operational challenges.
  • Increased operating expenses: The ineffectiveness of the backend system and the effort required to manually correct errors in the generated reports increased operational expenses and decreased overall profitability.
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The Approach

Through close collaboration with the client’s internal IT, finance, and data analytics teams, Curate’s consultants decided to focus on implementing backend optimization techniques, upgrading data workflows, and using serverless architecture to redesign and streamline the entire reporting process.

Business Outcomes

Decrease in Operational Costs


AWS Lambda and serverless architecture implementation reduced operational costs by 25% since resources were now scaled based on demand.

Improved Report Accuracy


Data validation automation processes decreased errors by 30% in financial reports, ensuring accuracy, consistency, and minimal manual intervention.

Scalable Infrastructure


The serverless architecture could handle growing data volumes without performance deterioration, allowing the client to generate reports efficiently even though their financial data was growing exponentially.

Customer Value

Faster Financial Reporting


The client could now deliver more timely and accurate insights to internal and external stakeholders. The optimized data extraction workflows and automated backend processes reduced the time required to generate financial reports by 40%.

Improved Decision Making


The client’s leadership team, especially, could make quicker data-driven decisions due to the enhanced financial reports. This also strengthened the institution’s reputation with external stakeholders.

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Conclusion

Curate collaborated with a large financial institution group to improve their data analytics and backend operations for quicker and more precise financial reporting. By utilising Python, FastAPI, AWS Lambda, and serverless architecture, Curate’s consultants assisted the customer in lowering operating expenses, speeding up report generation, and scaling their infrastructure to accommodate growing data volumes. This resulted in improved accuracy of financial insights, more business analytics value, improved customer trust, and better decision-making capabilities.

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