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For expense tools

Consistent categories, whatever the raw description says.

Expense tools bring together transactions from cards, accounts and payment providers. TXNORA gives each one a merchant, a category and labels, so overviews and review steps build on consistent data.

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The starting point

Categories are only as good as the raw data.

A purchase on Amazon shows up as “AMZN MKTP DE*X4H88291”, a payment to Adobe via PayPal as “PAYPAL *ADOBE”. Maintaining categories by hand, or deriving them from rules on raw text, means covering many spellings like these.

For review steps, it means whoever approves an expense often has to work out what is behind the line first. And category overviews drift as soon as a new spelling appears.

Raw descriptions as they arrive

  • PAYPAL *ADOBE – PAYPAL: Payment provider; ADOBE: Merchant

  • REWE SAGT DANKE 1834 – REWE: Merchant; SAGT DANKE: Extra text; 1834: Number

  • AMZN MKTP DE*X4H88291 – AMZN MKTP DE: Abbreviations; X4H88291: Reference

Example data. The breakdown explains the raw data and is not API output.

What structured context changes

More consistency in categories and review steps.

Three places where structured context fits into your expense tool.

  1. Consistent categories

    Every transaction gets a category from the same structure, so spending overviews build on consistent values.

  2. Labels for your own rules

    Labels such as SOFTWARE or RETAIL add context beyond the category. Use them as the basis for your own rules and filters.

  3. Clearer review steps

    Reviewers see a merchant and a category instead of a raw line. Confidence lets you route uncertain results to review.

Before and after

Expenses that make sense at a glance.

Three typical expenses in a review view: first as raw text, then with merchant, category, labels and confidence.

Expenses for review

Three example transactions

  1. Raw

    PAYPAL *ADOBE

    Structured

    Merchant: AdobeCategory: Software

    Amount: −59.49 EUR

  2. Raw

    REWE SAGT DANKE 1834

    Structured

    Merchant: REWECategory: Groceries

    Amount: −42.17 EUR

  3. Raw

    AMZN MKTP DE*X4H88291

    Structured

    Merchant: AmazonCategory: Retail

    Amount: −129.90 EUR

Example data from three typical transactions.

The confidence values are examples, not measured accuracy.

Relevant fields

What your expense tool needs.

For consistent overviews and review steps, category and labels come first.

  • Category

    category.id · category.name

    A consistent basis for spending overviews.

  • Labels

    labels

    Context for your own rules and filters.

  • Merchant

    merchant.name

    Shows reviewers where the expense came from.

  • Confidence

    confidence

    Routes uncertain results to review.

POST /v1/transactions/classifyExample response
{  "transaction_id": "txn_demo_001",  "merchant": {    "name": "Adobe"  },  "category": {    "id": "software",    "name": "Software"  },  "labels": [    "SOFTWARE",    "DIGITAL_SERVICE"  ],  "confidence": 0.984}

Relevant for this use case

Simplified example.

More use cases

Other products, the same foundation.

  • 01

    Fintechs

    Make transaction feeds easier for people to understand.

    PAYPAL *ADOBEbecomesMerchantAdobe

  • 02

    Accounting platforms

    Add merchant and category context before transactions enter a review workflow.

    REWE SAGT DANKE 1834becomesCategoryGroceries

  • 04

    Analytics products

    Work with structured transaction data in your own reporting experience.

    AMZN MKTP DE*X4H88291becomesMerchantAmazon

Test TXNORA with your product.

With free trial access, you see which fields matter for your use case. After your request, you receive access to the API, the developer documentation and our pricing.

Request trial accessAll use cases