<?xml version="1.0" encoding="UTF-8"?><oembed><type>video</type><version>1.0</version><html>&lt;iframe src=&quot;https://www.loom.com/embed/ab0d7c6335fa48ef8302120016b37eb0&quot; frameborder=&quot;0&quot; width=&quot;1920&quot; height=&quot;1440&quot; webkitallowfullscreen mozallowfullscreen allowfullscreen&gt;&lt;/iframe&gt;</html><height>1440</height><width>1920</width><provider_name>Loom</provider_name><provider_url>https://www.loom.com</provider_url><thumbnail_height>1440</thumbnail_height><thumbnail_width>1920</thumbnail_width><thumbnail_url>https://cdn.loom.com/sessions/thumbnails/ab0d7c6335fa48ef8302120016b37eb0-02252eadb754b0d9.gif</thumbnail_url><duration>153.901</duration><title>Introducing minFraud Risk Score Reasons 🚀</title><description>Hi, I&apos;m Miguel Atienza, Director of Product at MaxMind. Today, I introduce a new beta feature - minFraud Risk Score Reasons, providing understandable reasons for every score. Learn how to use this data for forensic investigations, post-incident analysis, pattern analysis over time, and to enhance your machine learning models. Your feedback on how this feature can assist you is highly appreciated!</description></oembed>