211 US class action settlements are open right now, 155 need no receipt. I scrape and score the whole board in one HTTP request.

Description and use

An automated scraper parses the ClassAction.org board, normalizes settlement data, flags key attributes, scores opportunities, and outputs a ranked list of claimable settlements, enabling users to act quickly.

Problem

Users struggle to identify claimable class action settlements quickly because the public board is a wall of cards that must be compared manually, leading to missed opportunities.

Outcome

The scraper processes ~210 rows in seconds, identifies 155 settlements that require no proof and 118 that pay cash, and produces a ranked list of the most urgent claims, saving users time and money.

Workflow

• 1. Send a single GET request to ClassAction.org to retrieve the board HTML.
• 2. Parse each settlement card to extract payout text, deadline, and proof‑of‑purchase requirement.
• 3. Convert payout ranges into numeric min/max values and compute deadline metrics (days left, urgency bucket).
• 4. Flag attributes: proofRequired, payoutKind, isNoProofCash.
• 5. Calculate claimScore (0–100) based on payout size, proof requirement, time window, and freshness.
• 6. Sort settlements by claimScore and output the top rows.
• 7. Provide official claim‑site URLs for direct filing.
• 8. Run the scraper via an Apify Actor with configurable inputs (e.g., noProofOnly, cashOnly, maxDaysLeft).
• 9. Schedule daily runs with Slack/email notifications for imminent deadlines.
• 10. Monitor with 13 assertions to detect markup changes before users see stale data.

Evidence

The scraper does three things per card: Parse the messy bits… Flag what matters… Score it.

Source

When ClassAction.org changes its markup — and it will — the gate turns red before a user sees an empty dataset.

Source