---
title: "Financial data scraping"
description: "Financial data scraping for rate tables, investor relations pages, filings, fund data and central bank releases. Tables as JSON, documents as Markdown."
canonical: https://www.zenrows.com/use-cases/financial-data-scraping
updated: 2026-10-18
---

# Financial data scraping

Financial data scraping collects public financial information from websites, such as interest rate tables, investor relations pages, regulatory filings, fund data and central bank releases. Zenrows fetches those pages, including protected and JavaScript-heavy ones, and returns tables as JSON and documents as clean Markdown, ready for analyst models, fintech products and compliance records.

## Sample output: a bank's mortgage rate table

```json
{
  "source_url": "https://www.example-bank.co.uk/mortgages/rates",
  "captured_at": "2026-10-16T07:30:04Z",
  "proxy_country": "gb",
  "tables": [
    {
      "dimensions": { "rows": 3, "columns": 5, "heading": true },
      "heading": ["Product", "Initial rate", "Fixed until", "Max LTV", "Product fee"],
      "content": [
        { "Product": "2 year fixed", "Initial rate": "4.19%", "Fixed until": "31/12/2028", "Max LTV": "75%", "Product fee": "£999" },
        { "Product": "5 year fixed", "Initial rate": "4.05%", "Fixed until": "31/12/2031", "Max LTV": "75%", "Product fee": "£999" },
        { "Product": "2 year tracker", "Initial rate": "4.44%", "Fixed until": "n/a", "Max LTV": "60%", "Product fee": "£0" }
      ]
    }
  ]
}
```

Illustrative output of the tables output filter on a rates page: every HTML table on the page comes back as rows keyed by column heading, with the capture time added by your pipeline.

## What finance teams collect from the web

Banks, fintechs, analysts and market data firms read the same handful of page types again and again. Most of the value sits in tables and short documents that change on a known calendar.

| Job | Data you collect | Typical sources |
|---|---|---|
| Rate and fee comparison | Savings, mortgage, loan and card rates, fees, product terms | Bank and lender product pages, comparison sites such as comparis.ch |
| Investor relations monitoring | Earnings dates, results releases, presentations, event calendars | Company IR pages and RSS feeds, GlobeNewswire, PR Newswire |
| Filings and register lookups | Filing indexes, firm register entries, disciplinary records | SEC EDGAR, FCA Financial Services Register, FINRA BrokerCheck |
| Central bank and macro releases | Rate decisions, statement text, release calendars | Federal Reserve, ECB, Bank of England, national statistics offices |
| Fund and ETF data | NAV, holdings, ongoing charges, factsheet fields | Asset manager fund pages, Morningstar |
| Market news and commentary | Headlines, tickers mentioned, publication time | CNBC, MarketWatch, Seeking Alpha, finanzen.net, Business Standard |

## Why finance web scraping breaks

Financial sites are often protected, built around JavaScript tables, and published on a schedule your downstream systems depend on. A failed run the morning of a rate decision or results day matters more than a failed run on a quiet week.

| What breaks | What it looks like | How Zenrows handles it |
|---|---|---|
| Cloud IPs get challenged | A scraper that worked from an office network hits Cloudflare challenges on IR pages once it moves to a cloud provider | Zenrows' stealth gets through Cloudflare, Akamai, Imperva and similar protections |
| Tables drawn by JavaScript | The rate or holdings table is missing from the raw HTML | Adaptive Stealth Mode renders the page when it needs a browser, and the tables output filter returns each table as JSON rows |
| Data inside interactive reports | Figures sit in an embedded dashboard that needs clicks before the table appears | [Browser](/products/browser) drives a real browser with Playwright or Puppeteer through the clicks |
| Rates that vary by country | A bank shows different products or no rates at all to foreign visitors | Geotargeting picks the country the request comes from, so you see the page a local visitor sees |
| Release-day timing | Thousands of IR pages must be checked within minutes of a release window | Batch runs the full watchlist as one job, with results by webhook |
| Vendor security review | Procurement asks for an SLA, SOC 2 and ISO 27001 before go-live | Zenrows provides an SLA and SOC 2 and ISO 27001 documentation for enterprise agreements |

## How to scrape financial data with Zenrows

1. **Sort your sources by shape.** Tables (rates, fund data), documents (releases, statements) and interactive pages (dashboards, search forms) each take a different request.
2. **Pull tables as JSON.** Send rate and fund pages to Fetch with the tables output filter, and use geotargeting for country-specific products.
3. **Collect documents as Markdown.** Run Crawl on an IR newsroom or a central bank's press section with Markdown output to get every release as clean text.
4. **Drive interactive pages.** Use Browser for reports and register searches that need clicks or form input.
5. **Schedule and diff.** Re-run your watchlist through Batch on the publishing calendar, and compare each run with the last to catch new releases and rate changes.

```python Python
# pip install requests
import requests

params = {
    "apikey": "YOUR_ZENROWS_API_KEY",
    "url": RATES_PAGE_URL,  # a bank or lender's rate table
    "mode": "auto",  # Adaptive Stealth Mode
    "proxy_country": "gb",
    "outputs": "tables",
}
response = requests.get("https://api.zenrows.com/v1/", params=params)
for table in response.json()["tables"]:
    print(table["heading"])
    for row in table["content"]:
        print(row)
```

```bash cURL
# Collect every press release in an investor relations newsroom as Markdown
curl -X POST "https://api.zenrows.com/v1/crawls" \
  -H "X-API-Key: YOUR_ZENROWS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "IR_NEWSROOM_URL",
    "max_items": 500,
    "max_pages": 700,
    "include_patterns": ["/news-releases/", "/press-releases/"],
    "output_format": "markdown"
  }'
```

```bash CLI
npx -y @zenrows/cli init
# Read a central bank statement as clean Markdown
zenrows fetch STATEMENT_URL --output markdown
# Pull every table from a UK rates page as JSON
zenrows extract RATES_PAGE_URL --outputs tables --proxy-country gb
```

```text Claude Code (MCP)
# Connect once
claude mcp add zenrows -e ZENROWS_API_KEY=YOUR_ZENROWS_API_KEY -- npx -y @zenrows/mcp

# Then ask:
Use Zenrows to read the Bank of England's latest monetary policy summary as Markdown, then list the Bank Rate decision, the vote split and the date of the next announcement.
```

## Which Zenrows product fits

| If you need to | Use | Why |
|---|---|---|
| Read a rate table or fund page as JSON | [Fetch](/products/fetch) | The tables output filter returns every table on the page as rows keyed by heading |
| Collect every release in an IR or press section | [Crawl](/products/crawl) | Follows the section's links and returns each matching page as Markdown |
| Check thousands of IR pages on results day | [Batch](/products/batch) | The whole watchlist runs as one job, with results by webhook |
| Get figures out of a dashboard or register search | [Browser](/products/browser) | A hosted browser you drive with Playwright or Puppeteer |
| Test a source before writing code | [CLI](/cli) | One command returns the page as Markdown |

## What success looks like

| Measure | What good looks like | How to check it |
|---|---|---|
| Release lag | A new release or rate change reaches your system within one check cycle of going live | Compare the publication time on the page with the time your record was created |
| Table fidelity | Parsed tables carry the same rows and values the page shows | Compare row counts and a sample of cells against the live page each week |
| Watchlist coverage | Every IR page and rate page on the list returns content on every run | Alert on any source with no successful response in the latest run |
| Change detection precision | Alerts fire for new content, not for rotating banners or timestamps | Review a sample of alerts and count the ones with no substantive change |
| Audit trail | Each stored value traces back to a URL, a capture time and the stored page | Pick random records and confirm the source snapshot can be retrieved |

## Financial data scraping FAQ

### What financial data can be scraped from the web?

Anything published on a public page: interest rates and fees, product terms, earnings dates, press releases, investor presentations, fund factsheets, register entries, central bank statements and macro release calendars. Quotes and price history are covered on the stock prices page , and hiring or pricing signals for investors on the alternative data page .

### Is web scraping for finance legal?

Collecting publicly available financial information is common in research, product comparison and compliance work. Respect each site's terms, check licensing on market data you plan to redistribute, and avoid personal data you have no basis to process. Rules vary by country and use, so confirm your use with your legal team.

### How do I monitor thousands of investor relations pages?

Keep a list of IR URLs and RSS feeds per company, and send the full list to Batch on a schedule, more often around earnings season. Store each response, compare it with the previous version, and raise an alert only when a new release, event or document link appears.

### How do I get tables out of financial web pages?

Add the tables output filter to a Fetch request. Zenrows returns every HTML table as JSON, with column headings and one object per row. For a single table among many, name the cells you want with CSS selectors. For tables inside an interactive report, open the page with Browser and read the table after the clicks.

### Can Zenrows read central bank and regulator websites?

Yes. Many of these sites are plain HTML, and some sit behind bot protection or render with JavaScript. Adaptive Stealth Mode starts with the lightest setup and escalates to browser rendering and stealth only when a page needs it, so one request works for both. Crawl a press section with Markdown output to keep every statement as clean text.

### Can I scrape stock market data and financial news together?

Yes, with one request per page type. Quote pages and price history usually need browser rendering, which Adaptive Stealth Mode adds only when the page needs it. News pages from CNBC, MarketWatch or finanzen.net come back as Markdown, ready to tag with the tickers they mention. Keep both in one Batch job so a results-day run covers prices and coverage at the same time.

### What security documentation does Zenrows provide for banks?

Zenrows provides an SLA and SOC 2 and ISO 27001 documentation for vendor risk assessments under enterprise agreements. Storing the source URL and capture time with every record also gives auditors the lineage of each value from your own database.

## Related use cases

- [Alternative data for investors](https://www.zenrows.com/use-cases/alternative-data.md): Hiring, pricing, app ranks, reviews and store counts per ticker, collected weekly as point-in-time series.
- [Public records API](https://www.zenrows.com/use-cases/public-records-api.md): Permits, violations, licenses and registry filings from city, county and state portals, returned as JSON on demand.
- [Insurance data scraping](https://www.zenrows.com/use-cases/insurance-data.md): Competitor quotes, policy terms, rate filings and property risk data, captured per test profile.

Start free: https://app.zenrows.com/register. Docs: https://docs.zenrows.com