Design and Implementation of a Streamlit-based Web Application for Automated Debit Calculation from PDF Bank Statements

In an era marked by the rapid advancement of digital finance, individuals, freelancers, and small business owners are increasingly reliant on electronic documentation for tracking and managing their financial transactions. Among the most ubiquitous of these documents are bank e-statements, typically issued monthly in the Portable Document Format (PDF). While these digital statements provide a convenient and standardized way of recording financial activity, extracting meaningful insights—such as total debit amounts—often requires tedious manual inspection. For users without access to advanced financial management tools or accounting software, reconciling transactions from such statements can be time-consuming and errorprone. This paper introduces the design and development of a lightweight, web-based application that automates the extraction and summation of debit transactions from bank statements provided in PDF format. Built entirely in Python, the tool leverages three primary open-source libraries: Streamlit for building an interactive and user-friendly interface, pdfplumber for parsing and extracting tabular data from PDF files, and pandas for efficient data manipulation and analysis. The combination of these libraries results in a streamlined, accessible application that requires no programming expertise on the part of the end user. The core functionality of the application revolves around the automatic identification of columns typically labeled “Debit” or “Withdrawals.” Upon uploading a password-free bank e-statement, the application parses each page, extracts tables, and converts them into pandas DataFrames. It then scans these tables to detect column headers or data fields related to debit transactions. Only those rows containing valid debit entries—based on keyword recognition and numerical value validation— are retained. The application then computes the cumulative sum of the debit values and displays both the filtered transactions and the total debited amount in a clear and concise format. The motivation behind this solution is rooted in enhancing financial transparency and simplifying daily bookkeeping practices. Whether for tracking personal expenses or preparing monthly ledgers for small enterprises, this tool provides an accessible and cost-effective alternative to complex accounting software. Furthermore, by automating the extraction and calculation process, the risk of human error is significantly reduced, and users gain valuable time that would otherwise be spent combing through pages of transactional data. Another notable feature of this application is its adaptability. Given that bank statement formats can vary significantly between institutions, the implementation includes flexible parsing logic that searches for a variety of keyword patterns associated with debit activity. This ensures that the application remains robust across different formats and layouts, provided that the statements are text-based and contain recognizable table structures.