Revolutionizing banking with data warehousing solutions

Problem statement

Limited Data: Traditional systems often rely on manual data scraping and only provide limited data for analysis, resulting in incomplete and unreliable insights.

Time-Consuming: Manually scraping data is time-consuming and can slow down the loan analysis process.

Inaccurate Results: Traditional systems are prone to errors and inconsistencies, which can lead to inaccurate results and poor decision making.

Lack of Integration: Traditional systems often don't integrate with other data sources, making it difficult to derive meaningful insights from multiple sources.

Limited Scalability: Traditional systems are often not equipped to handle large volumes of data, making it difficult to scale the analysis process as needed.

Solution overview

We have worked with the client in implementing AWS data warehousing system for collecting their loan data and using them for data analysis.

The datawarehouse solution is capable of handling large data files with micro-level data in a matter of seconds, saving time and effort.

Downloaded end-user loan details as initial step using a scheduled job using SQL Runner Scripts tool.

Built a pipeline to extract the downloaded file and upload it into multiple tables. Uploaded data files are moved to an archive folder. Designed Dimensions, HUB, and SAT tables using the liquibase tool.

Data uploaded to base tables are aggregated and validated using AWS Lambda functions. Build and deploy changes using AWS Code Commit and Code pipeline.

Business impact

Improved data collection and processing: Data engineering models automate and streamline the collection and processing of loan data, reducing manual effort and increasing efficiency.

Enhanced risk management: With the ability to access loan data and perform sophisticated analysis, organizations can better understand and manage risk in their loan portfolio quickly and easily.

Enhanced data insights: By using advanced data processing techniques and visualizations, data engineering models can provide deeper insights into loan data, allowing organizations to make more informed decisions.

Improved data accessibility: With a centralized and organized repository of loan data, stakeholders across the organization can access and utilize data more easily, driving better collaboration and decision-making.

Increased competitiveness: By leveraging data to drive business decisions, organizations using data engineering models have a competitive advantage over those relying on traditional, manual methods.

What our Clients say

"They are very responsive and able to shift focus quickly as we have needed it for business reasons."

"They [OptiSol Business Solutions] have been very friendly and they kept up with the work. They did the job as we had discussed and as everything was arranged."

“I can't speak enough about how pleased I am with their process; it was seamless and efficient.”

"What impressed me the most was their willingness to go above and beyond what was asked."

"We're constantly impressed with their speed and quality of the development that they achieve on a consistent basis."

The best part about working with OptiSol Business Solutions is their commitment to being readily available to their partners. They’re even willing to work into late hours of the night depending on time differences. The solution they produced has already doubled efficiency levels.