categorization

name: categorization description: Danish merchant knowledge and transaction categorization rules. Reference this when classifying transactions into spending categories.

Categorization

Purpose

Provides Claude with Danish merchant knowledge and categorization rules for classifying bank transactions into spending categories.

Categories

15 Danish spending categories with subcategories:

Merchant Pattern Database

Map raw transaction text patterns to normalized merchant names and categories. Patterns use * as wildcard.

Dagligvarer (Groceries)

Transport

Abonnementer (Subscriptions)

Restauranter (Dining)

Shopping

Bolig (Housing)

Sundhed (Health)

Underholdning (Entertainment)

Rejser (Travel)

Personlig pleje

Uddannelse (Education)

Normalization Rules

Before matching patterns, normalize the raw transaction text:

1. Convert to uppercase for matching
2. Replace Danish characters: Ø → OE, Æ → AE, Å → AA (for pattern matching only — preserve originals in raw_text)
3. Collapse multiple spaces into one
4. Trim leading/trailing whitespace

Transaction Type Detection

Use the transaction description prefix to identify the payment method:

Matching Order

When categorizing a transaction, check these sources in order — stop at the first match:

1. learnings/categorization.md — Check for user corrections learned from previous sessions (confidence 1.0). These take precedence over all other rules.
2. learnings/merchants.md — Check for merchant name aliases to normalize the raw text before pattern matching.
3. merchant-overrides.csv — Check for a matching raw_pattern (confidence 1.0). These are learned from previous user corrections within the same session.
4. Static pattern database — Check the merchant pattern tables above (confidence 1.0 exact / 0.8 partial).
5. Intelligent classification — No pattern match, but Claude can infer category from transaction context (confidence 0.5–0.7).
6. Fallback — Cannot determine category — assign to "Andet" (confidence 0.0).

Confidence Scoring

Assign a confidence score to each categorization:

When manual_override is TRUE for a transaction, always use the user's category regardless of what patterns suggest.

Learning from Corrections

When a user manually corrects a transaction's category, the correction should be saved to learnings/categorization.md so future sessions automatically apply the learned rule.

How to record a categorization learning:

1. Open learnings/categorization.md
2. Append a new row to the Learnings table with:
   • Date: Today's date (YYYY-MM-DD)
   • Pattern: The normalized raw text pattern to match (uppercase, wildcards allowed)
   • Merchant: The normalized merchant name
   • Category: The user-corrected category
   • Subcategory: The user-corrected subcategory
   • Context: Brief note about why this correction was made

Example learning entry:

| 2026-01-15 | *NETFLIX* | Netflix | Underholdning | Streaming | User: "Netflix er underholdning, ikke abonnement" |

Also update merchant-overrides.csv for the current session:

1. Take the corrected transaction's raw_text from transactions.csv
2. Normalize it: uppercase, trim whitespace, collapse multiple spaces
3. Use this as the raw_pattern
4. Use the user-corrected merchant, category, and subcategory from categorized.csv
5. Set created_at to the current timestamp
6. Only append if no row with the same raw_pattern already exists

Ambiguous Merchants

• Netto: Always Dagligvarer, even though they sell non-grocery items
• 7-Eleven: Dagligvarer (most purchases are food/drink)
• Amazon: Default to Shopping unless description contains "Prime" or "Kindle" (then Abonnementer or Uddannelse)
• Normal: Shopping → Andet (sells mixed categories)
• Matas: Sundhed → Apotek (primarily health/beauty)
• Wolt: Restauranter → Takeaway unless the Wolt+ subscription charge (then Abonnementer)

Examples

Example 1: Clear Match

raw_text: "NETTO FO 1234 KØBENHAVN"
normalized: "NETTO FO 1234 KOEBENHAVN"
pattern match: *NETTO* → Netto
result: category=Dagligvarer, subcategory=Supermarked, merchant=Netto, confidence=1.0

Example 2: PBS Subscription

raw_text: "PBS FITNESS WORLD"
normalized: "PBS FITNESS WORLD"
pattern match: *FITNESS*WORLD* → Fitness World
result: category=Abonnementer, subcategory=Fitness, merchant=Fitness World, confidence=1.0, is_recurring=TRUE

Example 3: Intelligent Classification

raw_text: "RESTAURANT COFOCO KBH"
normalized: "RESTAURANT COFOCO KBH"
no pattern match — but "RESTAURANT" prefix suggests dining
result: category=Restauranter, subcategory=Restaurant, merchant=Cofoco, confidence=0.6

Example 4: Income

raw_text: "Løn fra Arbejdsgiver ApS"
prefix match: "Løn fra" → salary
result: category=Indkomst, subcategory=Løn, merchant=Arbejdsgiver ApS, confidence=1.0