An anti-fraud system designed to protect against fraud has many running parts: anti-malware programs with centralized management and advanced authentication; devices that allow you to visualize the process of signing a document with an electronic signature. All these are fraud prevention tools and this is quite correct. However, let’s break fraud detection down in more detail.

Anti-fraud: what is it?

When paying online, you probably had to enter a code from a text message to confirm that it was you who was using your card and not an intruder stealing your funds. This is the part of fraud detection of banking systems

Anti-fraud is an automated fraud detection program that evaluates bank or online transactions according to certain criteria. If a transaction does not meet those criteria, a more thorough check is performed, and fraud prevention tools make the decision to allow or block the transaction. Since 2004, the use of fraud prevention tools has been mandatory worldwide.

How do anti-fraud checks work?

Let’s look at an example of fairly common fraud in the USA or Europe. A fraudster wanted to buy a promotional item from an online store and use stolen credit card data for that. It seems to be easy: he needs to create several accounts and pay for the purchases once per card. As a result, both the actual cardholders and the owners of the business may suffer damage. It is to prevent such situations that fraud detection is used by banks.

Initial verification and filters

However, the rules of any risk management software algorithms, for example, by Covery, will prevent this from happening, because fraud prevention tools will be the basis for the primary verification.

Applying machine learning

Keep in mind that an anti-fraud system is a constantly tunable and updatable algorithm, which starts working better if it is adjusted correctly with time. Machine learning is used for this purpose: Artificial Intelligence risk management software generates patterns based on historical data on user behavior to make further real-time predictions.

Final Check

After the filter check, the anti-fraud system sets tags for each transaction:

  • Green – “approved,” fraud is unlikely. For example, when the user pays monthly utility bills and the transactions are about the same amount.
  • Yellow – “verification required,” there is a possibility of fraud. This label can occur in fraud prevention tools when small, identical amounts are sent from one account to several other accounts. 
  • Red – “fraud risk” when the user’s actions are atypical and fraud prevention tools sound the alarm. 

However, the final decision – to block the transaction or take other actions – is made by not the Covery anti-fraud system but by the anti-fraud analyst.