{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/bfb9829813a740229c51648a8de1b441\" frameborder=\"0\" width=\"1920\" height=\"1440\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1440,"width":1920,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1440,"thumbnail_width":1920,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/bfb9829813a740229c51648a8de1b441-87be377c011f46cf.gif","duration":134.356,"title":"Operationalizing Fraud Models","description":"In this video, I discuss the final steps of operationalizing a fraud model, focusing on how to effectively manage user reviews. I explain the importance of automatic banning for certain users while ensuring we still gather verified labels for accuracy. I also share the probability thresholds we use for alerts and how we simulate the process in our test database. Please take a moment to review the thresholds and provide any feedback."}