Personal Spending Pattern Analysis from Bank Statement Data

Content

{
  "variables": [
    "bank_statement_lines",
    "user_context"
  ],
  "prompt_text": "Analyze the following personal bank statement data and produce a structured spending analysis. Categorize every transaction, identify spending patterns, flag anomalies, and provide actionable recommendations.\n\nBank statement data:\n{{bank_statement_lines}}\n\nUser context (optional):\n{{user_context}}\n\nOutput a JSON object with the following structure:\n{\n  \"summary\": { \"total_income\": number, \"total_spending\": number, \"balance\": number },\n  \"categories\": { \"category_name\": { \"total\": number, \"transactions\": [{\"date\": \"YYYY-MM-DD\", \"description\": \"...\", \"amount\": number}] } },\n  \"patterns\": [\"pattern description\", ...],\n  \"anomalies\": [{\"description\": \"...\", \"transaction\": \"...\", \"reason\": \"...\"}],\n  \"recommendations\": [\"actionable tip\", ...]\n}",
  "example_output": "{\n  \"summary\": { \"total_income\": 2656.43, \"total_spending\": 1080.65, \"balance\": 396.84 },\n  \"categories\": {\n    \"eating_out\": { \"total\": 116.80, \"transactions\": [{\"date\": \"2026-06-05\", \"description\": \"McDonald's\", \"amount\": -7.41}] }\n  },\n  \"patterns\": [\"High concentration of eating-out spend in first week after payday\"],\n  \"anomalies\": [{\"description\": \"Large one-off debt settlement\", \"transaction\": \"Multiple debt payments totalling €663.61\", \"reason\": \"Debt cleanup concentrated in single month\"}],\n  \"recommendations\": [\"Set up automated savings transfer on payday to reduce lifestyle creep\"]\n}",
  "model_compatibility": [
    "claude-sonnet-4-6",
    "gpt-4o",
    "gemini-1.5-pro"
  ]
}