Revolutionizing Banking withData Warehousing Solutions

Business Challenges

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.

Inefficient Reporting: Traditional systems often lack the ability to generate meaningful and actionable reports, leaving loan analysts without the information they need to make informed decisions.

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.

Increased cost savings: By automating manual processes and reducing errors, organizations can save time and reduce costs associated with loan data analysis.

Technology Stack

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.

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

5yrs

in AI & ML
Engineering

40+

AI & ML
Projects for
reputed Clients

50+

AI & ML
Engineers

Awarded as Winner among 1000 contestants at TechSHack Hackathon

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