There are as many forms of fraud as there are human activities, practically. In addition, it is a practice that goes back to the beginnings of our economic activity as human beings, since there have always been people interested in obtaining some benefit at the expense of the harm of others.
Today, frauds are increasingly elaborate. If we restrict ourselves only to the online environment, we can find increasingly elaborate actions such as phishing, mainly, which offers hooks that are increasingly faithful to the original they want to impersonate. The staging is increasingly convincing, more complex, and therefore more difficult to detect with the naked eye.
Fraud against financial institutions is something very serious. Of course, fraud through fraudulent emails is also serious, but in the case of large companies, a scam can mean millions of euros in losses.
Attackers increasingly use more sophisticated systems to find vulnerabilities in the financial “value chain”, using advanced Machine Learning algorithms to find the weakest points where attacks will begin. Criminals use all the computing power at their fingertips to go for the weakest link and from there jump to other links in the chain.
To understand why machine learning is crucial to fraud management, we need to understand how fraud can be “parameterized”:
Faced with the growing threat of new forms of fraud, many of them supported by machine learning to find new vulnerabilities, the way is to bet on machine learning itself to defend itself, not only from these attacks but also from its speed and amazing capacity. adaptation and evolution.
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