Building a Neobank-Quality Transaction Feed: A Developer's Guide

Users of digital banks like Revolut, Monzo, and N26 have come to expect transaction feeds that go far beyond a list of dates and amounts. They expect merchant logos, clean names, spending categories, and location data, all rendered instantly and beautifully. If you are building a fintech product that displays transactions, this is the bar you need to clear. In this guide, we will break down what makes a great neobank transaction feed, the data architecture behind it, and how to build one using the Easy Enrichment API.

What Makes a Great Transaction Feed

Open any neobank app and you will notice the transaction feed is not just a ledger. It is a rich, interactive interface that tells users where their money went at a glance. The best transaction feeds share several characteristics that set them apart from traditional banking apps.

Revolut displays merchant logos next to every transaction, groups spending by day with running totals, and lets users tap into a transaction to see the merchant on a map. Monzo uses color-coded category icons, real-time notifications with merchant names, and a spending breakdown by category baked right into the feed. N26 takes a minimal approach but still shows clean merchant names, categorized spending, and sub-account allocation for each transaction.

The common thread is that none of these apps show the raw transaction string from the bank. They all run their data through an enrichment layer that transforms cryptic payment descriptions into structured, visual information.

Key Data Points Users Expect

When designing a transaction feed, these are the fields users now consider table stakes:

• Merchant name: A clean, human-readable name instead of "POS DEBIT AMZN MKTP US*2K1AB"
• Merchant logo: A recognizable brand icon that makes scanning the feed instant
• Category: Shopping, Groceries, Transport, Entertainment, and so on
• Subcategory: More granular classification like "Online Marketplace" or "Coffee Shop"
• Location: City or address where the transaction occurred, when available
• Transaction type: Card payment, bank transfer, direct debit, ATM withdrawal

Without these data points, your transaction feed is just a spreadsheet. With them, it becomes the core UX surface that keeps users coming back to your app.

Architecture: From Bank Feed to Rich UI

The data pipeline for a neobank-quality transaction feed has three stages: ingestion, enrichment, and rendering. Getting this architecture right is critical for both performance and data quality.

Stage 1: Ingestion

Raw transactions arrive from your bank connectivity provider, whether that is Plaid, TrueLayer, Tink, or a direct banking API. These transactions contain a description string, an amount, a date, and sometimes an MCC code. The description is typically a messy string set by the payment processor, not the merchant. You store these raw transactions in your database as-is, preserving the original data for audit purposes.

Stage 2: Enrichment

This is where the transformation happens. Each raw transaction is sent through an enrichment API that returns structured merchant data, categories, logos, and more. The enrichment should happen asynchronously in a background job, not in the user's request path. Store the enriched data alongside the raw transaction so you never need to re-enrich the same transaction twice.

Stage 3: Rendering

Your frontend pulls the enriched transaction data and renders it with logos, category icons, and color coding. The API response should be optimized for the feed view, returning only the fields the UI needs, with pagination for historical data.

Using Easy Enrichment API

The Easy Enrichment API transforms raw bank transaction descriptions into rich, structured data with a single API call. Here is what a typical enrichment flow looks like:

// Send a raw transaction to the enrichment API
const response = await fetch('https://api.easyenrichment.com/enrich', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer YOUR_API_KEY',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    description: "POS DEBIT AMZN MKTP US*2K1AB0C9Z",
    amount: -67.43,
    date: "2025-03-01"
  })
});

const enriched = await response.json();
// Returns:
// {
//   "merchant_name": "Amazon",
//   "category": "Shopping",
//   "subcategory": "Online Marketplace",
//   "logo_url": "https://logo.clearbit.com/amazon.com",
//   "mcc_code": "5942",
//   "is_subscription": false,
//   "confidence": 0.97
// }

For bulk processing, use the batch endpoint to enrich up to 100 transactions in a single request:

const batchResponse = await fetch('https://api.easyenrichment.com/enrich/batch', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer YOUR_API_KEY',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    transactions: rawTransactions.map(t => ({
      description: t.description,
      amount: t.amount,
      date: t.date,
      mcc: t.mcc_code
    }))
  })
});

const { results } = await batchResponse.json();

Building the Transaction Feed Component

Here is a React component that renders an enriched transaction feed with merchant logos, category icons, and color coding. This pattern works with Next.js, Remix, or any React-based framework.

import { useState, useMemo } from 'react';

const CATEGORY_CONFIG = {
  'Shopping':       { color: '#f59e0b', icon: 'shopping-bag' },
  'Groceries':      { color: '#22c55e', icon: 'shopping-cart' },
  'Transport':      { color: '#3b82f6', icon: 'car' },
  'Entertainment':  { color: '#a855f7', icon: 'film' },
  'Restaurants':    { color: '#ef4444', icon: 'utensils' },
  'Subscriptions':  { color: '#06b6d4', icon: 'refresh' },
  'Bills':          { color: '#f97316', icon: 'file-text' },
  'Health':         { color: '#ec4899', icon: 'heart' },
};

function TransactionFeed({ transactions }) {
  const [search, setSearch] = useState('');
  const [categoryFilter, setCategoryFilter] = useState(null);

  const filtered = useMemo(() => {
    return transactions.filter(txn => {
      const matchesSearch = !search ||
        txn.merchant_name.toLowerCase().includes(search.toLowerCase()) ||
        txn.category.toLowerCase().includes(search.toLowerCase());
      const matchesCategory = !categoryFilter ||
        txn.category === categoryFilter;
      return matchesSearch && matchesCategory;
    });
  }, [transactions, search, categoryFilter]);

  // Group transactions by date
  const grouped = useMemo(() => {
    const groups = {};
    filtered.forEach(txn => {
      const date = txn.date;
      if (!groups[date]) groups[date] = [];
      groups[date].push(txn);
    });
    return Object.entries(groups).sort(([a], [b]) =>
      new Date(b) - new Date(a)
    );
  }, [filtered]);

  return (
    <div>
      <input
        type="text"
        placeholder="Search transactions..."
        value={search}
        onChange={e => setSearch(e.target.value)}
        className="w-full p-3 rounded-lg bg-zinc-900 border
                   border-zinc-800 text-white mb-4"
      />

      <div className="flex gap-2 mb-6 overflow-x-auto">
        {Object.keys(CATEGORY_CONFIG).map(cat => (
          <button
            key={cat}
            onClick={() => setCategoryFilter(
              categoryFilter === cat ? null : cat
            )}
            className={`px-3 py-1 rounded-full text-sm ${
              categoryFilter === cat
                ? 'bg-white text-black'
                : 'bg-zinc-800 text-zinc-400'
            }`}
          >
            {cat}
          </button>
        ))}
      </div>

      {grouped.map(([date, txns]) => (
        <div key={date} className="mb-6">
          <h3 className="text-sm text-zinc-500 mb-2">
            {formatDate(date)}
          </h3>
          {txns.map(txn => (
            <TransactionRow key={txn.id} txn={txn} />
          ))}
        </div>
      ))}
    </div>
  );
}

function TransactionRow({ txn }) {
  const config = CATEGORY_CONFIG[txn.category] || {
    color: '#71717a', icon: 'circle'
  };

  return (
    <div className="flex items-center gap-3 py-3
                    border-b border-zinc-800/50">
      {txn.logo_url ? (
        <img
          src={txn.logo_url}
          alt={txn.merchant_name}
          className="w-10 h-10 rounded-full object-cover"
        />
      ) : (
        <div
          className="w-10 h-10 rounded-full flex items-center
                     justify-center text-white text-sm font-bold"
          style={{ backgroundColor: config.color }}
        >
          {txn.merchant_name?.charAt(0)}
        </div>
      )}

      <div className="flex-1 min-w-0">
        <p className="text-white font-medium truncate">
          {txn.merchant_name}
        </p>
        <p className="text-sm text-zinc-500">{txn.category}</p>
      </div>

      <span className={`font-mono text-sm ${
        txn.amount > 0 ? 'text-green-400' : 'text-white'
      }`}>
        {txn.amount > 0 ? '+' : ''}
        {txn.amount.toFixed(2)}
      </span>
    </div>
  );
}

Adding Merchant Logos

Merchant logos are the single biggest visual upgrade you can make to a transaction feed. The Easy Enrichment API returns a logo_url field for each enriched transaction, pointing to a high-quality merchant logo. When a logo is not available, the component above falls back to a colored circle with the merchant's initial, which still looks far better than no visual at all.

To handle logo loading gracefully, add an onError handler to the image element that swaps in the fallback initial. Preload logos for the visible viewport and lazy-load the rest. For repeat merchants, the browser cache handles subsequent loads, but you can also store logo URLs in your database to avoid unnecessary enrichment calls.

Category Icons and Color Coding

Color coding transactions by category lets users scan their feed and immediately identify spending patterns. The CATEGORY_CONFIG map in the component above assigns a unique color to each category. Use these colors consistently across your app: in the transaction feed, in spending charts, in budget progress bars, and in category breakdown views.

For icons, libraries like Lucide React or Heroicons provide clean, consistent icon sets. Map each category to an icon that users will intuitively associate with that spending type. A shopping bag for retail, a car for transport, utensils for restaurants. Keep the icon set small and consistent rather than trying to match every possible subcategory.

Search and Filtering with Enriched Data

Raw bank transactions are nearly impossible to search because the descriptions are inconsistent and full of codes. Enriched data changes this completely. When every transaction has a clean merchant name and a category, you can offer users powerful search and filtering.

The component above demonstrates client-side search and category filtering. For production apps with thousands of transactions, move filtering to the server side. Build database indexes on merchant_name and category columns, and support full-text search on the enriched merchant name. You can also let users filter by amount range, date range, transaction type, and subscription status, all fields that come back from the enrichment API.

// Server-side filtering with enriched fields
app.get('/api/transactions', async (req, res) => {
  const { search, category, startDate, endDate, minAmount, maxAmount } = req.query;

  let query = db('transactions')
    .where('user_id', req.user.id)
    .orderBy('date', 'desc');

  if (search) {
    query = query.where('merchant_name', 'ilike', `%${search}%`);
  }
  if (category) {
    query = query.where('category', category);
  }
  if (startDate && endDate) {
    query = query.whereBetween('date', [startDate, endDate]);
  }
  if (minAmount) {
    query = query.where('amount', '>=', parseFloat(minAmount));
  }

  const transactions = await query.limit(50);
  res.json({ transactions });
});

Performance: Caching and Batch Enrichment

Performance is critical for transaction feeds. Users scroll through hundreds of transactions and expect instant rendering. Here are the strategies that matter most.

Cache Enrichment Results

Never enrich the same transaction description twice. Store the mapping from raw description to enriched data in a cache table or Redis instance. When a new transaction arrives with a description you have seen before, pull the enriched data from cache instead of making an API call. In practice, this reduces API calls by 60-80% because many users shop at the same merchants repeatedly.

Batch Enrichment for Historical Data

When a user first connects their bank account, you may receive 6 to 12 months of historical transactions. Do not enrich these one by one. Use the batch endpoint to process up to 100 transactions per request, and run the job in a background worker. Show the user a progress indicator while their historical data is being enriched, and render transactions as they become available.

Frontend Optimization

Virtualize long transaction lists using a library like react-window or TanStack Virtual. This ensures that only the transactions visible in the viewport are rendered in the DOM, even if the user has thousands of transactions loaded. Combine this with pagination on the API side, loading 50 transactions per page and fetching more as the user scrolls.

// Enrichment cache layer
async function enrichWithCache(description, amount, date) {
  const cacheKey = `enrich:${description}`;
  const cached = await redis.get(cacheKey);

  if (cached) {
    return JSON.parse(cached);
  }

  const result = await fetch('https://api.easyenrichment.com/enrich', {
    method: 'POST',
    headers: {
      'Authorization': 'Bearer YOUR_API_KEY',
      'Content-Type': 'application/json'
    },
    body: JSON.stringify({ description, amount, date })
  }).then(r => r.json());

  // Cache for 30 days
  await redis.set(cacheKey, JSON.stringify(result), 'EX', 2592000);

  return result;
}

Putting It All Together

Building a neobank-quality transaction feed comes down to three things: good data, good architecture, and good UI. The data comes from enrichment. The architecture ensures enrichment happens efficiently through caching and background processing. The UI uses logos, colors, and clean typography to make transactions scannable at a glance.

Start with the enrichment layer. Once you have structured merchant data flowing through your system, the UI patterns follow naturally. Category breakdowns, spending charts, subscription tracking, and search all become straightforward when every transaction has a clean merchant name, a category, and a logo.

The gap between "bank app" and "neobank" is largely a data quality gap. Close that gap with enrichment, and your transaction feed will look and feel like it belongs in the same category as Revolut and Monzo.