Many companies use measures to detect fraud that are now outdated and do not work properly. This includes, for example, the search for malware prevention or users who are redirected from known phishing pages. The approach may seem acceptable, but it doesn’t really reduce fraud losses. This is due to the following reasons:
Signature Based Recognition Is Unreliable
With this way, a large part of the malware remains undetected. And detected malware prevention is often associated with a large increase of false results. This means that signature based approaches do not particularly reduce the risk.
Compromised Login Information Is Not The Same With Fraud
Even if a company detects phishing sites, that does not in itself help in detecting attempted malware. After all, what can a company do with a compromised user account? After all, a company is not going to lock out its own customers.
In order to separate malware from legitimate transfers reliably and efficiently, companies should switch their measures from signature-based to behavior based. Instead of focusing on infected user accounts, you have to watch out for unusual, anomalous behavior and activities that are not expected and are probably not legitimate.
Fraud prevention no longer depends on knowing which identities have been compromised. A lower rate of false data means less wasted time following wrong tracks. More correct messages – A higher rate of correctly detected fraud attempts results in fewer fraud losses and better overall business results.
More Security Of Action
The more precisely a transaction can be assessed as suspicious, the more specifically companies can take appropriate measures, for example approve, deny, demand. Simply reacting on the malware infections, phishing and compromised login information cannot reduce losses due to fraud. Instead, businesses should focus on distinguishing malware from transactions. This can only be achieved through behavior-based networks. In this way, attempts at malware can be reliably identified and prevented.