Clawdhub Submission

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:

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

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