Automated Bank Statement Analysis

Overview

This project provides an automated solution for extracting, analyzing, and summarizing financial transactions from bank statements. The system processes multiple PDF bank statements to derive key financial insights, such as total deposits, withdrawals, recurring expenses, and outstanding loans. The goal is to streamline financial assessment, particularly for loan evaluations.

Features

• Automated Data Extraction: Uses OCR and NLP techniques to extract transactions from PDF statements.

• Financial Summarization: Computes monthly deposits, withdrawals, net balances, and categorizes expenses.

• Loan Eligibility Assessment: Identifies existing loans and analyzes cash flow patterns.

• Anomaly Detection: Flags unusual spending behaviors.

• Interactive Data Visualization: Generates structured financial summaries for better decision-making.

Technologies Used

• Python (pandas, pdfplumber, dateutil, re)

• Data Processing & Analysis

• Machine Learning for Anomaly Detection (Future Scope)

Usage

• Place bank statement PDFs in the data/ directory.

• Run the script to extract and analyze transactions.

• Review the structured financial summary output.

Future Enhancements

• Implement a web-based dashboard for interactive data visualization.

• Integrate machine learning models for credit risk scoring.

• Expand support for additional bank statement formats.