Combine multiple Trustfull products to accurately score user identity

Assess risk across all touchpoints

As fraudsters employ ever more sophisticated techniques to conceal their activities, distinguishing them from legitimate users becomes increasingly challenging. In this context, the importance of comprehensive digital footprint analysis cannot be overstated. Trustfull addresses this need by aggregating a wide range of trust and risk indicators within a unified framework. This approach enables a thorough examination of data related to email, phone numbers, IP addresses, devices, and browser usage. By analyzing these diverse data points collectively, Trustfull enhances the accuracy of risk assessments, making it possible to identify and mitigate fraudulent behavior more effectively. This holistic scrutiny of digital behavior is crucial for maintaining the integrity of online platforms and protecting genuine users from fraud.

Varieties of Identity Fraud

Identity Theft

This fraud emerges when an offender illicitly utilizes a mix of someone else's personal details acquired through unauthorized means.

The compromised information may encompass:

  • Full name
  • Birthdate
  • Social Security or tax ID number
  • Photograph from an official identification document
  • Telephone number
  • Residential address

Synthetic Identity Fraud

This type of fraud, closely related to identity theft, is characterized by the fraudster's inventive merging of various data pieces. These elements could originate from entirely fabricated identities or be a compilation of details from multiple legitimate individuals. The fraudster skillfully assembles these diverse pieces, such as names, birthdates, and social security numbers, to construct an identity profile that convincingly mimics a real person's identity. This sophisticated tactic is aimed at bypassing security measures and verification processes, making it particularly challenging for systems and authorities to detect and prevent. The creation of such composite identities enables fraudsters to engage in illicit activities under the guise of a credible persona, complicating the efforts to trace and address the fraud.

Social Engineering

In schemes involving manipulative deception, the fraudster employs a blend of persuasion and psychological tactics to convince the target to voluntarily divulge personal identity information on a digital platform. This usually occurs during the initial stages of engagement, such as the account signup or registration process. The fraudster's objective is to acquire enough personal details to gain unauthorized access to the victim's account or to create a new, fraudulent account under the victim's name.

Once the necessary information is obtained, the fraudster proceeds to usurp control of the account, either to conduct unauthorized transactions, steal funds, or further perpetrate fraud that could harm the victim's financial standing or reputation.

More Than Just Product Integration

The onboarding solution will allow you to look at cross-product signals and check for consistency or inconsistency between multiple products.

Inconsistency ranges from:

  • Partials: Using OSINT techniques we retrieve the partial phone number using the email address such as 39.....58 or the email address starting from the phone number [email protected]
  • Name extraction: The name extracted from the email address and the name provided by the user such as the risk signal RE024 "Email Name Inconsistency" or the same logic applied to the phone number.
  • Image analysis: using profile picture extraction we can cross-reference faces extracted from images and verify that the images are coming from the same person.

Extensive score

The score developed for onboarding incorporates the majority of the rules established for individual products but also implements extensive additional checks. These checks are specifically designed to analyze cross-product behavior, aiming to identify and highlight inconsistencies in identity.

Although each product score might be positive, a low level of consistency could suggest that the identity is fabricated and the signals are not interconnected.

What’s Next

Reason Codes: receive via API all the information about risk and trust singnal