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”:
- There are too many unique cases of fraud to go after them all and to have concrete solutions for all of them.
- Fraud patterns change rapidly, so any non-anticipatory action will be out of date in no time.
- We are talking about people dedicated entirely to studying susceptible systems to specifically compromise the security of the weakest points.
- A (good) scam will mimic the behaviors of legitimate customers, so if we have overly strict or intrusive countermeasures, our best customers may be penalized without reason.
Machine Learning solves all these problems. To get started:
- Minimizes the need to manually review existing vulnerabilities, as well as helping to find new weaknesses.
- You can learn to ‘devise’ new forms of attack by looking at actual attacks by cybercriminals – something similar to what we described when we explained the concept of Honeypot.
- Improves human decision-making, providing greater precision.
- You can reduce false positives through behavior analysis.
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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