The ability to understand the underlying factors related to risk allows companies to take a clear and concise approach to fraud prevention. By turning to an identity verification and fraud prevention platform that enables adaptive identity risk scoring, organizations can strengthen their risk models and improve their customer authentication processes at any step of the customer lifecycle.
In an ever-evolving fraud landscape, it is vital to spot and stop fraud in order to effectively mitigate risk and meet compliance standards while still enabling a frictionless experience for legitimate customers.
How Is Identity Risk Scoring Used?
Identity risk scoring is typically used when developing comprehensive scoring models and risk profiles in order to help meet regulatory guidelines in industries that recommend risk assessments of new accounts. Risk scoring can be used to evaluate high-risk customer segments and gives an organization the resources to further evaluate identities that have been previously verified but possess suspicious and high-risk attributes.
Since there are multiple forms of fraudulent attacks attempted on any given organization, tools are needed that evolve as new fraud rules and mandates are put in place. In order to accomplish these goals, it is best practice to use a system built on adaptive scoring.
Adaptive scoring allows companies to create scoring models based on unique business rules and on developing fraud tactics. This type of system allows risk managers to pass, fail or escalate transactions using actionable information gathered about each potential transaction. Additionally, risk managers can group identities into distinct categories, which again goes toward improving and streamlining compliance efforts.
Since scoring models can be created on-demand, organizations are able to adjust tactics, as needed, to stay in line with new and updated regulations ahead of shifting fraud techniques.
IDology recently launched a new leadership video on how we are completely redefining identity risk scoring. View this video below: