These anomalous actions may be signs of potential criminal attempts and can be flagged by the system to require an additional human inspection. The idea behind implementing machine learning in fraud detection is to scan data regarding money transactions or interactions with applications, platforms, and corporate networks to probe behaviors that look out of the ordinary. Therefore, we do not provide labeled data and let the system define eventual categories and relationships.Ĭonsidering these trends, as well as the growing importance for companies and public institutions to protect their sensitive data, networks, and financial assets, it should come as no surprise that machine learning's anomaly detection capabilities have been widely leveraged to prevent and counter the aforementioned threats.
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