ABOUT CUSTOMER

Founded in 2008, this leading financial services organization is dedicated to empowering women by offering accessible and affordable financial solutions. With a strong presence in rural and semi-urban regions, the company now operates across 17 states in India. Through its inclusive approach, it continues to foster sustainable economic development and uplift underserved communities.

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Industry : Microfinance

Location : Bengaluru, India.

Application & Services Used : Electroneek

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CHALLENGE

• The organization has 800+ branches across India with data stored in unstructured formats.
• Branch data was spread across multiple folders without proper organization.
• The organization needed a one-click access solution for each branch’s data.
• This involved merging branch data into individual PDFs with clickable links.
• Over 800 forms had to be consolidated into a master file, demanding significant time and effort due to poor formatting.

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SOLUTION

Data Filtering and Preparation:

A bot was developed using Python and ElectroNeek to filter raw Excel data and create individual datasets for each branch.

Migration to Master File

Another bot was used to extract data from each branch’s PAF sheet and consolidate it into a single master Excel file.

Document Merging

For each branch, a bot gathered all related documents and merged them into a single PDF for streamlined access.

Link Generation and Mapping

The merged PDFs were uploaded to Work Drive, and bots generated downloadable links which were inserted into an Excel file alongside corresponding branch names.

Final Integration

The master Excel file was mapped with the file containing branch-wise download links, allowing users to access all data with a single click per branch.

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RESULTS

• Centralized Access: All branch-related data is now available in a single master Excel file, improving data accessibility.
• Quick Navigation: Users can instantly access each branch’s documents through clickable PDF links.
• Time Efficiency: Automated data merging and link generation significantly reduced manual effort and time.
• Improved Accuracy: Automation minimized human errors in data mapping and consolidation.
• Simplified Workflow: The use of Python and ElectroNeek bots streamlined the entire process, making data retrieval simple and efficient.

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