Acquire valuable customers

Automatically confirm the identity of prospectuses and analyse their behaviour on the purchasing path.

 

A Kaspersky Lab study on cyber threats targeted at financial institutions shows that every fourth bank in the world has a problem with successfully verifying the identity of an online banking customer.

At the same time, almost 60% of banks expect increasing losses from fraud in the coming years. This shows how important an identity verification should be in the security strategy.

Therefore, one of the elements of the BlueBooster platform is the anti-fraud module, which allows you to automatically and effectively identify the customer, thereby reducing the risk of losses on the bank's side.

Mechanism capabilities

BlueBooster enables:

  • the verification of contact details provided by those interested (e-mail address and phone number),
  • the confirmation of the prospectus's identity using a PBL verification transfer (pay by link).

The system allows you to send messages via a corresponding channel and conditions the transition to the next step in a form with positive verification.

In practice, it may be, for example, that the customer will proceed to the next step only after he enters the code received in the SMS in the right window.

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Customer behaviour analysis

Another element of the anti-fraud module is a mechanism analysing the behaviour of each customer on the sales path in real time.

The system checks such aspects as the content of the answers entered by the user in the form and the amount of time it takes the user to fill them in.

In this way, the bank can be sure that it is dealing with a real person and can modify the sales path, adapting it to the customer's behaviour.

Determining the risk degree

Fraud mechanisms are configured by the bank. The system classifies conclusions based on the behavioural analysis and user data.

The following data are analysed: environmental data (computer or other devices, operating system, browser version), data entered by users, and their behaviours (e.g. how long the user fills out every field in the form).

Fraud verification can be extended to libraries of suspicious domains, IBAN account numbers, etc.

On the basis of the collected data, the system informs you if there is a suspicion of fraud or not. For example, a person presenting themselves as "Jan", whose personal ID number indicates female gender will be assessed as "fraud".

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