Free tools to analyze personal spending habits from a bank statement file

Analyzing "spending habits" (as opposed to a single month's total) benefits specifically from a tool that can run real code across many months of data — trends, category growth, and merchant frequency are the kind of multi-row analysis a spreadsheet formula handles awkwardly but pandas handles naturally. Both major free assistants can do this via their code-execution features, at no cost beyond a free account.

Getting real pattern analysis from a statement file

1. Export multiple months as CSV and redact sensitive fields. Most banks let you export several months at once — black out or crop account and routing numbers before uploading, since the analysis doesn't need them.

2. Upload to ChatGPT's Advanced Data Analysis or Claude's code execution tool. Both let the model write and run real pandas code against your file rather than reasoning about the numbers in chat.

3. Ask specifically for habit-revealing questions, not just totals. "Show me which spending categories grew month-over-month" or "list my 10 most frequent merchants by transaction count" use the code-execution capability to surface actual patterns.

# This is the kind of code ChatGPT/Claude run against your
# uploaded, redacted statement CSV -- not a chat-based guess
import pandas as pd

df = pd.read_csv("statement.csv", parse_dates=["date"])
df["month"] = df["date"].dt.to_period("M")

# Most frequent merchants -- reveals recurring habits
top_merchants = df["description"].value_counts().head(10)

# Category spend trend across months
trend = df.groupby(["month", "category"])["amount"].sum().unstack()

The distinction between "total" and "habit" questions matters for tool choice: a single total is easy with any method, but genuine pattern discovery across months needs real code running real aggregations — exactly what these free code-execution features are built for.