| 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:
| Category | Subcategories | Description |
|---|
| Bolig | Husleje, El, Vand, Varme, Forsikring | Housing costs |
| Dagligvarer | Supermarked, Specialbutik | Groceries |
| Transport | Offentlig, Bil, Taxi, Cykel | Transportation |
| Abonnementer | Streaming, Fitness, Software, Telefon | Subscriptions |
| Restauranter | Restaurant, Café, Takeaway | Dining out |
| Shopping | Tøj, Elektronik, Bolig, Andet | Shopping |
| Sundhed | Apotek, Læge, Tandlæge | Health |
| Underholdning | Biograf, Koncert, Spil | Entertainment |
| Rejser | Fly, Hotel, Ferie | Travel |
| Børn | Daginstitution, Tøj, Legetøj | Children |
| Personlig pleje | Frisør, Kosmetik | Personal care |
| Uddannelse | Kurser, Bøger, Materialer | Education |
| Opsparing | Overførsler til opsparing | Savings transfers |
| Indkomst | Løn, Refusion | Income |
| Andet | Ukategoriseret | Other / fallback |
Merchant Pattern Database
Map raw transaction text patterns to normalized merchant names and categories. Patterns use * as wildcard.
Dagligvarer (Groceries)
| Pattern | Merchant | Subcategory |
|---|
*NETTO* | Netto | Supermarked |
*FØTEX* or *FOETEX* | Føtex | Supermarked |
*REMA*1000* or *REMA1000* | Rema 1000 | Supermarked |
*IRMA* | Irma | Supermarked |
*LIDL* | Lidl | Supermarked |
*ALDI* | Aldi | Supermarked |
*BILKA* | Bilka | Supermarked |
*MENY* | Meny | Supermarked |
*SPAR* | Spar | Supermarked |
*FAKTA* | Fakta | Supermarked |
*COOP* | Coop | Supermarked |
*DAGLIG*BRUGSEN* or *DAGLI*BRUGSEN* | Dagli'Brugsen | Supermarked |
*SUPER*BRUGSEN* | SuperBrugsen | Supermarked |
*KVICKLY* | Kvickly | Supermarked |
Transport
| Pattern | Merchant | Subcategory |
|---|
*DSB* | DSB | Offentlig |
*REJSEKORT* | Rejsekort | Offentlig |
*MOVIA* | Movia | Offentlig |
*Q8* | Q8 | Bil |
*CIRCLE*K* | Circle K | Bil |
*SHELL* | Shell | Bil |
*OK BENZIN* or *OK PLUS* | OK | Bil |
*UBER* | Uber | Taxi |
*TAXA* or *DANTAXI* | Taxa | Taxi |
*DONKEY*REPUBLIC* | Donkey Republic | Cykel |
Abonnementer (Subscriptions)
| Pattern | Merchant | Subcategory |
|---|
*NETFLIX* | Netflix | Streaming |
*SPOTIFY* | Spotify | Streaming |
*DISNEY*PLUS* or *DISNEYPLUS* | Disney+ | Streaming |
*HBO* or *MAX*STREAMING* | HBO Max | Streaming |
*VIAPLAY* | Viaplay | Streaming |
*TV2*PLAY* or *TV 2 PLAY* | TV2 Play | Streaming |
*YOUTUBE*PREMIUM* or *GOOGLE*YOUTUBE* | YouTube Premium | Streaming |
*APPLE*MUSIC* | Apple Music | Streaming |
*FITNESS*WORLD* | Fitness World | Fitness |
*FITNESS*DK* | Fitness DK | Fitness |
*SATS* | SATS | Fitness |
*TDC* or *YOUSEE* | TDC | Telefon |
*TELENOR* | Telenor | Telefon |
*TELIA* | Telia | Telefon |
*ADOBE* | Adobe CC | Software |
*MICROSOFT*365* or *MICROSOFT*OFFICE* | Microsoft 365 | Software |
*ICLOUD* or *APPLE.COM/BILL* | iCloud | Software |
*DROPBOX* | Dropbox | Software |
Restauranter (Dining)
| Pattern | Merchant | Subcategory |
|---|
*WOLT* | Wolt | Takeaway |
*JUST*EAT* | Just Eat | Takeaway |
*TOO GOOD TO GO* or *TOOGOODTOGO* | Too Good To Go | Takeaway |
*STARBUCKS* | Starbucks | Café |
*JOE*THE*JUICE* or *JOE & THE JUICE* | Joe & The Juice | Café |
*LAGKAGEHUSET* | Lagkagehuset | Café |
*MCDONALDS* or *MC DONALDS* | McDonald's | Restaurant |
*BURGER*KING* | Burger King | Restaurant |
Shopping
| Pattern | Merchant | Subcategory |
|---|
*H&M* or *H M * or *HM * | H&M | Tøj |
*ZALANDO* | Zalando | Tøj |
*IKEA* | IKEA | Bolig |
*ELGIGANTEN* | Elgiganten | Elektronik |
*POWER* | Power | Elektronik |
*NORMAL* | Normal | Andet |
*FLYING*TIGER* | Flying Tiger | Andet |
*SØSTRENE*GRENE* or *SOSTRENE*GRENE* | Søstrene Grene | Andet |
*AMAZON* | Amazon | Andet |
*JYSK* | Jysk | Bolig |
Bolig (Housing)
| Pattern | Merchant | Subcategory |
|---|
*ØRSTED* or *OERSTED* | Ørsted | El |
*HOFOR* | HOFOR | Vand |
*TRYG* | Tryg | Forsikring |
*TOPDANMARK* | Topdanmark | Forsikring |
*ALKA* or *CODAN* | Codan | Forsikring |
*HUSLEJE* or *FAST OVERFØRSEL*HUSLEJE* | Husleje | Husleje |
*NORLYS* | Norlys | El |
*EWII* | Ewii | El |
Sundhed (Health)
| Pattern | Merchant | Subcategory |
|---|
*APOTEK* | Apoteket | Apotek |
*MATAS* | Matas | Apotek |
*TANDLÆGE* or *TANDLAEGE* | Tandlæge | Tandlæge |
*LÆGE* or *LAEGE* | Læge | Læge |
Underholdning (Entertainment)
| Pattern | Merchant | Subcategory |
|---|
*NORDISK*FILM* | Nordisk Film | Biograf |
*CINEMAXX* | CinemaxX | Biograf |
*TICKETMASTER* | Ticketmaster | Koncert |
*BILLETLUGEN* | Billetlugen | Koncert |
Rejser (Travel)
| Pattern | Merchant | Subcategory |
|---|
*SAS* or *SCANDINAVIAN*AIRLINES* | SAS | Fly |
*NORWEGIAN* | Norwegian | Fly |
*RYANAIR* | Ryanair | Fly |
*AIRBNB* | Airbnb | Hotel |
*HOTELS.COM* or *BOOKING.COM* | Booking.com | Hotel |
Personlig pleje
| Pattern | Merchant | Subcategory |
|---|
*SEPHORA* | Sephora | Kosmetik |
*FRISØR* or *FRISOER* or *CUTTERS* | Frisør | Frisør |
Uddannelse (Education)
| Pattern | Merchant | Subcategory |
|---|
*SAXO* | Saxo | Bøger |
*AMAZON*KINDLE* | Amazon Kindle | Bøger |
Normalization Rules
Before matching patterns, normalize the raw transaction text:
- Convert to uppercase for matching
- Replace Danish characters:
Ø → OE, Æ → AE, Å → AA (for pattern matching only — preserve originals in raw_text)
- Collapse multiple spaces into one
- Trim leading/trailing whitespace
Transaction Type Detection
Use the transaction description prefix to identify the payment method:
| Prefix Pattern | Type | Notes |
|---|
Dankort-køb | Card payment (physical) | Danish debit card |
Visa-køb | Card payment (physical) | Visa card |
Overførsel | Bank transfer | Internal or external |
Fast overførsel | Standing order | Recurring transfer |
PBS or Betalingsservice | Direct debit | Subscriptions and bills |
MobilePay | Mobile payment | Person-to-person or merchant |
Løn fra | Salary | Income — categorize as Indkomst |
Hævning | ATM withdrawal | Categorize as Andet |
Matching Order
When categorizing a transaction, check these sources in order — stop at the first match:
- learnings/categorization.md — Check for user corrections learned from previous sessions (confidence 1.0). These take precedence over all other rules.
- learnings/merchants.md — Check for merchant name aliases to normalize the raw text before pattern matching.
- merchant-overrides.csv — Check for a matching
raw_pattern (confidence 1.0). These are learned from previous user corrections within the same session.
- Static pattern database — Check the merchant pattern tables above (confidence 1.0 exact / 0.8 partial).
- Intelligent classification — No pattern match, but Claude can infer category from transaction context (confidence 0.5–0.7).
- Fallback — Cannot determine category — assign to "Andet" (confidence 0.0).
Confidence Scoring
Assign a confidence score to each categorization:
| Match Type | Confidence | Description |
|---|
| Merchant override match | 1.0 | Transaction text matches a learned override in merchant-overrides.csv |
| Exact pattern match | 1.0 | Transaction text matches a known merchant pattern |
| Partial pattern match | 0.8 | Part of the text matches a known pattern |
| Intelligent classification | 0.5–0.7 | No pattern match, but Claude can infer from context |
| Unknown | 0.0 | Cannot determine category — assign to Andet |
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:
- Open
learnings/categorization.md
- 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:
- Take the corrected transaction's
raw_text from transactions.csv
- Normalize it: uppercase, trim whitespace, collapse multiple spaces
- Use this as the
raw_pattern
- Use the user-corrected
merchant, category, and subcategory from categorized.csv
- Set
created_at to the current timestamp
- 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
