AIApplied AI World
Beginner40 min

Create a Monthly Expense Report with Cursor AI

Build a script that reads bank exports, categorizes expenses, and generates a polished monthly report.

#cursor#python#finance#automation

Companion Video

What you’ll build

An automated expense report generator that takes a CSV bank export and produces a categorized monthly summary — ready to share or archive.

Prerequisites

  • Cursor installed
  • Python 3.10+
  • A CSV export from your bank (date, description, amount columns)
1

Create the project structure

mkdir expense-report
cd expense-report
cursor .

Ask Cursor to scaffold:

expense-report/
├── main.py
├── categorize.py
├── report.py
├── requirements.txt
└── data/
    └── transactions.csv
2

Load and clean the CSV

In main.py, load your bank export with pandas:

import pandas as pd

df = pd.read_csv("data/transactions.csv")
df["amount"] = df["amount"].astype(float)
df["date"] = pd.to_datetime(df["date"])
3

Categorize transactions

Create categorize.py with keyword-based rules:

CATEGORIES = {
    "groceries": ["walmart", "costco", "whole foods"],
    "dining": ["starbucks", "uber eats", "doordash"],
    "utilities": ["hydro", "internet", "mobile"],
}

Or ask Cursor to build smarter matching with fuzzy search.

4

Generate the report

In report.py, summarize by category:

summary = df.groupby("category")["amount"].sum().sort_values(ascending=False)
print(summary.to_string())

Add totals, top merchants, and biggest single purchases.

5

Export to PDF or HTML

Ask Cursor:

Add a function that exports the monthly summary to a clean HTML report with a title, date range, and category breakdown table.

Open the HTML in your browser or print to PDF.

What you learned

  • Loading and cleaning financial CSV data
  • Rule-based categorization with AI assistance
  • Generating readable reports from raw transactions

Next steps

  • Schedule monthly runs with a cron job
  • Email the report automatically
  • Add charts with matplotlib or Plotly

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