refund-radar

Scan bank statements to detect recurring charges, flag suspicious transactions, identify duplicates and fees, draft refund request templates, and generate an interactive HTML audit report.

Triggers

• "scan my bank statement for refunds"
• "analyze my credit card transactions"
• "find recurring charges in my statement"
• "check for duplicate or suspicious charges"
• "help me dispute a charge"
• "generate a refund request"
• "audit my subscriptions"

Workflow

1. Get Transaction Data

Ask user for bank/card CSV export or pasted text. Common sources:

• Apple Card: Wallet → Card Balance → Export
• Chase: Accounts → Download activity → CSV
• Mint: Transactions → Export
• Any bank: Download as CSV from transaction history

Or accept pasted text format:

2026-01-03 Spotify -11.99 USD
2026-01-15 Salary +4500 USD

2. Parse and Normalize

Run the parser on their data:

python -m refund_radar analyze --csv statement.csv --month 2026-01

Or for pasted text:

python -m refund_radar analyze --stdin --month 2026-01 --default-currency USD

The parser auto-detects: - Delimiter (comma, semicolon, tab) - Date format (YYYY-MM-DD, DD/MM/YYYY, MM/DD/YYYY) - Amount format (single column or debit/credit) - Currency

3. Review Recurring Charges

Tool identifies recurring subscriptions by: - Same merchant >= 2 times in 90 days - Similar amounts (within 5% or $2) - Consistent cadence (weekly, monthly, yearly) - Known subscription keywords (Netflix, Spotify, etc.)

Output shows: - Merchant name - Average amount and cadence - Last charge date - Next expected charge

4. Flag Suspicious Charges

Tool automatically flags:

Flag Type	Trigger	Severity
Duplicate	Same merchant + amount within 2 days	HIGH
Amount Spike	> 1.8x baseline, delta > $25	HIGH
New Merchant	First time + amount > $30	MEDIUM
Fee-like	Keywords (FEE, ATM, OVERDRAFT) + > $3	LOW
Currency Anomaly	Unusual currency or DCC	LOW

5. Clarify with User

For flagged items, ask in batches of 5-10:

• Is this charge legitimate?
• Should I mark this merchant as expected?
• Do you want a refund template for this?

Update state based on answers:

python -m refund_radar mark-expected --merchant "Costco"
python -m refund_radar mark-recurring --merchant "Netflix"

6. Generate HTML Report

Report saved to ~/.refund_radar/reports/YYYY-MM.html

Copy template.html structure. Sections: - Summary: Transaction count, total spent, recurring count, flagged count - Recurring Charges: Table with merchant, amount, cadence, next expected - Unexpected Charges: Flagged items with severity and reason - Duplicates: Same-day duplicate charges - Fee-like Charges: ATM fees, FX fees, service charges - Refund Templates: Ready-to-copy email/chat/dispute messages

Features: - Privacy toggle (blur merchant names) - Dark/light mode - Collapsible sections - Copy buttons on templates - Auto-hide empty sections

7. Draft Refund Requests

For each flagged charge, generate three template types: - Email: Formal refund request - Chat: Quick message for live support - Dispute: Bank dispute form text

Three tone variants each: - Concise (default) - Firm (assertive) - Friendly (polite)

Templates include: - Merchant name and date - Charge amount - Dispute reason based on flag type - Placeholders for card last 4, reference number

Important: No apostrophes in any generated text.

CLI Reference

# Analyze statement
python -m refund_radar analyze --csv file.csv --month 2026-01

# Analyze from stdin
python -m refund_radar analyze --stdin --month 2026-01 --default-currency CHF

# Mark merchant as expected
python -m refund_radar mark-expected --merchant "Amazon"

# Mark merchant as recurring
python -m refund_radar mark-recurring --merchant "Netflix"

# List expected merchants
python -m refund_radar expected

# Reset learned state
python -m refund_radar reset-state

# Export month data
python -m refund_radar export --month 2026-01 --out data.json

Files Written

Path	Purpose
~/.refund_radar/state.json	Learned preferences, merchant history
~/.refund_radar/reports/YYYY-MM.html	Interactive audit report
~/.refund_radar/reports/YYYY-MM.json	Raw analysis data

Privacy

• No network calls. Everything runs locally.
• No external APIs. No Plaid, no cloud services.
• Your data stays on your machine.
• Privacy toggle in reports. Blur merchant names with one click.

Requirements

• Python 3.9+
• No external dependencies

Repository

https://github.com/andreolf/refund-radar