Skip to content

Fraud Prevention

2 posts with the tag “Fraud Prevention”

Keeping Claz useful for people, not bots

Claz is built for people – someone selling a couch, looking for an apartment, replying to an ad nearby. Lately, a growing share of our traffic hasn’t been people at all. It’s been bots.

Keeping Claz useful for people, not bots

AI and large language models have made large-scale scraping cheap and easy. The result is more automated traffic, often not gentle. That worries us for three reasons:

– Abuse of real users’ data. Listings posted in good faith can be harvested in bulk and reused for scams – fake offers, phishing, impersonation.

– Reliability. A flood of automated requests can slow Claz down for the people actually here to buy and sell.

– Fairness. Scrapers take everything and give nothing back.

The hard part isn’t blocking traffic – it’s blocking the right traffic, without annoying real users. So, at a high level:

– Smarter traffic limits, so no single source can hammer the site or vacuum pages at machine speed.

– Abuse detection that separates automated harvesting from normal human use.

– Verification only when needed – a quick check shown when traffic looks suspicious, invisible in normal use.

Probably nothing. Browsing, searching, and posting should feel the same. On rare occasions a user might be asked to confirm they’re human – that’s just the system being careful, and it should pass quickly.

We’d rather hear that a measure is in your way than have you quietly leave. If anything slows you down or blocks something legitimate, please tell us – we’ll tune it. Getting this balance right is ongoing work, and your feedback is the best signal we have.

Using AI and Machine Learning to Improve User Safety

Content moderator reviewing listings

Heads up: we’re building a Machine Learning system to fight scam artists.

In the past, we’ve relied on our community and moderators to categorize listings and report suspicious ads. We’ve made great progress by automating most of this work with a flexible rules engine. Today, we’re feeding years of training data into an experimental distributed Machine Learning System to help us scale this process to the next level.

We’ll be posting updates on the progress and some stats as more data becomes available.