A user sharing lessons from their time at Airbnb outlines four kinds of fraud signals: details of the event, the account’s state, the sequence and speed of activity, and connections between accounts. They describe an early system that extracted features, trained models on labeled outcomes and produced fraud-risk scores.

Their advice: shorten the evaluation loop, inspect results by segment, and check offline tests for duplicates, overlap between training and test data, and features that accidentally include future information. They also recommend keeping raw and converted currency amounts, timestamping external data such as exchange rates, and investing in clean data and reliable labels.