Data Pipeline for Banking Sector to Analyze Mortgage Reports

Business Challenges

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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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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

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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.

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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.

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Build and deploy changes using AWS Code Commit and Code pipeline.

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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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

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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

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