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For fintechs

Transaction feeds people can read.

Your users see what the bank sends: abbreviations, payment providers and reference numbers. TXNORA turns that into a merchant, a category and labels you can display directly.

Request trial accessSee before and after

The starting point

Transaction descriptions are rarely written for people.

A line like “PAYPAL *ADOBE” names the payment provider first and the actual merchant second. “AMZN MKTP DE*X4H88291” is mostly abbreviations and a reference. Anyone scanning their feed has to decode lines like these themselves.

For your product, that means transactions are harder to recognize, and overviews by merchant or category need mapping logic of your own.

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

From raw description to a readable line.

Three things a structured feed makes possible.

  1. Merchants instead of abbreviations

    A normalized merchant name like “Adobe” can replace the raw description in your interface. You can still use the original line for details or search.

  2. Categories for overviews

    One consistent category per transaction is the basis for spending overviews, filters and month-to-month views in your app.

  3. Confidence for your display logic

    With the confidence value, you decide when to show a result and when to stay with the raw description.

Before and after

How your feed reads with context.

Three typical transactions in a feed: first raw, then with merchant, category and labels from TXNORA.

Transactions in your app

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 feed needs.

For display in a feed, merchant and category come first. Labels and confidence help with filters and with deciding what to show.

  • Merchant

    merchant.name

    The name your users see in the feed.

  • Category

    category.id · category.name

    The basis for spending overviews and filters.

  • Labels

    labels

    Extra context, for example for hints or search filters.

  • Confidence

    confidence

    Helps decide whether to show the result or the raw description.

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.

  • 02

    Accounting platforms

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

    REWE SAGT DANKE 1834becomesCategoryGroceries

  • 03

    Expense tools

    Bring more consistency to spending categories and review queues.

    PAYPAL *ADOBEbecomesLabelsSOFTWARE · DIGITAL_SERVICE

  • 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