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case-studies-fintech
CX Transformation with a Custom Financial Software Solution for a Leading FinTech Company
Centralized Credit Data Management to Power Machine Learning in Financial Services
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Overview
The client is a prominent South African fintech company that helps customers with debt relief and financial advice. They were looking for a more intelligent way of managing customer credit data prompting the need for a custom financial software solution built specifically for the FinTech space, which would involve having such data centralized to generalize machine learning models, marketing campaigns, and strategic decision-making. Pulling credit data from bureaus was prohibitively expensive and inefficient. That's where L&G Consultancy came in. We architected a low-cost, secure financial software solution using Azure’s technology stack using Azure's technology stack that streamlined data access, improved processing speed, and enhanced data security.
The Challenges
The client faced several hurdles
Inefficient SQL Queries
Reporting delays were hampering timely decisions.
High Data Retrieval Costs
Repeated credit score requests drained almost all profits.
Non-scalable
The current setup could not handle the growing volume of data.
No Automation
Data handling was manual, increasing the chances of errors while slowing down the process.
The Objective
Building a Cost-Effective, Scalable Credit Data Framework
To reduce credit data retrieval costs and improve efficiency, we aimed to build a smarter system one that eliminated duplicate requests, centralized storage, and provided seamless access across platforms. Security, speed, and scalability were at the core of our custom-built financial software for the client.
The Solution
We developed a custom data management framework for this FinTech solution, focused on eliminating redundancy and improving credit data access. It was developed to the following process.
PROCESS
Linked customers with the financial institutions they were tied to by using Azure Data Factory mapping, producing accurate ID-institution pairs.
With Azure Databricks, we deduplicated credit request data to ensure that only unique queries went to the bureau.
Processed credit data was sorted into batch files specific to institutions and accordingly redistributed, thereby minimizing unnecessary traffic.
Instead of normal SQL storage, we processed data directly within Databricks and used Unity Catalog for role-based access control—enhancing security and compliance.
- Azure Data Factory: orchestrating customer and institution mapping
- Azure Databricks: data cleansing, processing, deduplication, and redistribution
- Azure Synapse: scalable analytics and reporting
- Unity Catalog: role-based access for secure data governance
The Impact
This secure financial software solution empowered the client with long-term operational efficiency and a solid data infrastructure.
By streamlining the way credit data was retrieved, the client was able to work more efficiently and cut down on costs. The updated system not only made data processing faster and smoother, but also added the flexibility they needed to adapt quickly and scale operations as the business grew.
20% decrease in batch processing costs.
Improved data flexibility, speed, and accuracy.
Increased operational efficiency through automation.
Enhanced security through controlled data access.
Scalable infrastructure supporting future growth.
Conclusion
The client benefited from reduced costs as well as a future-ready decision-making foundation built by L&G Consultancy, offering a different perspective on credit data. The project reflects how thoughtful innovation and precise execution can drive powerful change especially in industries where data accuracy and speed are everything especially when developing FinTech applications that use machine learning for smarter decisions.
