Revolutionizing Banking withData Warehousing Solutions

Process Flow: The Visual Story

Technology Stack

Problem Statement

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

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

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

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

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

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Solution Overview

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

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

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

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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.

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

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Business Impact

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

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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.

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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.

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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.

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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.

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Testimonials of Our Happy Clients

Rick Tigges

Rick Tigges

CFO, Bemodo

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

Ryan McClellan

CEO, iLookin.com, LLC

"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."
Chris Gordon

Chris Gordon

CEO, Wait for me

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

Oli Robinson

CEO, Rule:Fresh

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

Dan Talken

Founder, My Equipment Library, LLC

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

Ken Kisner

Molecule Corp

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.

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Our Award-Winning Team

A seasoned AI & ML team of young, dynamic and curious minds recognized with global awards for making significant impact on making human lives better

Awarded Bronze Trophy at CII National competition on Digitization, Robotics & Automation (DRA) – Industry 4.0

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5yrs

in AI & ML
Engineering

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40+

AI & ML
Projects for
reputed Clients

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50+

AI & ML
Engineers

Awarded as Winner among 1000 contestants at TechSHack Hackathon

Trusted and Proven Engagement Model

  • A nondisclosure agreement (NDA) is signed to not disclose any sensitive information revealed over the course of doing business together.
  • Our NDA-driven process is established to keep clients’ data and IP safe and secure.
  • The solution discovery phase is all about knowing your target audience, writing down requirements, and creating a full scope for the project.
  • This helps clarify the goals, and limitations, and deliver quality products & services.
  • Our engagement model defines the project size, project development plan, duration, concept, POC etc.
  • Based on these scenarios, clients may agree to a particular engagement model (Fixed Bid, T&M, Dedicated Team).
  • The SOW document shall list details on project requirements, project management tools, tech stacks, deliverables, milestones, timelines, team size, hourly/monthly rate cards, billable hours and invoice details.
  • On signing the SOW, an official project kick-off meeting shall be initiated.
  • Our implementation approach, ecosystem, tools, solutions modelling, sprint plan, etc. shall be discussed during this meeting.
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