Project Overview

This project transforms raw financial statements and banking PDFs into structured data pipelines for automated processing, analytics, and reporting.

Business Challenge

Financial statements stored in PDF and semi-structured formats created manual processing overhead, slowed

analysis, and limited real-time visibility into financial data.

My Contribution

I developed an end-to-end financial data pipeline using Python, MySQL, and Power BI to automate data

extraction, transaction processing, database storage, and dashboard-based analytics.

Key Features

• Automated PDF statement extraction

• Transaction categorization

• Structured MySQL data storage

• Duplicate transaction prevention

• Interactive Power BI dashboards

• Monthly trend analysis

• Category-wise transaction visualization

• Financial activity monitoring

• Time-based filtering and drill-down analysis

Business Outcome

The solution significantly reduced manual reporting effort, improved processing consistency, and enabled faster access to structured financial insights through automated analytics dashboards.

Final Conclusion

This project demonstrates how data engineering, ETL automation, and business intelligence tools can modernize financial data processing and improve analytical decision-making workflows.