Login & authentication

Knowledge-based login authentication (KBA) questions verify a user’s identity by requiring them to provide unique information that only they would know. There are two main types of KBA: static and dynamic. Static KBA questions are user-defined, while dynamic KBA asks questions based on identity data analysis. Though, at one point, Knowledge-based login authentication was considered a valid way to create additional security, this form of login authentication has two fatal flaws. First, asking customers to answer questions before validating their identity causes unnecessary friction. Second, many data points used for KBA have been leaked through data breaches or can be pulled from social media, giving bad actors the information they need to guess the answers with alarming accuracy.

Companies seeking to strengthen account security and prevent account takeovers (ATOs) must monitor for signs of fraudulent activity such as repeated failed login authentication attempts or unusual login locations. Detecting suspicious login behavior enables organizations to catch illegitimate activity before it negatively impacts the business. Here are some of the other most common signs of fraudulent login attempts:

• Odd login times
• Login attempts from an unfamiliar IP address
• Login attempts from an unrecognized device
• Abnormal session duration
• Attempt fails to trigger or bypasses multi-factor login authentication (MFA)

These signs can arise for various reasons and should not be used as the sole confirmation that a fraudulent login attempt has occurred. However, they should invoke further security measures, such as mandatory re-authentication with a multi-factor authentication (MFA) application or a required password reset.

Biometric login authentication verifies users with extremely high levels of accuracy and security using unique biological identifiers. The most common forms of this are fingerprint scans, facial recognition, and voice recognition. Beyond biological features, this form of login authentication can also analyze user behavior post-login and detect anomalous activity based on deviations from historical user data. Biometric authentication is highly praised as a form of identity verification for its convenience and extreme resilience against digital identity fraud. There are no passwords for users to worry about, and liveness detection algorithms make it very hard for fraudsters to spoof identity verification systems with AI deepfakes

Most competitors try to solve login security problems with limited, point-based solutions that have poor accuracy and a lack of real-time functionality. There are many tools for email authentication, phone verification, and fingerprint scanning, but few complete platforms. This leaves organizations relying on a piecemeal approach that is cumbersome for users, offers poor security, and creates additional costs for businesses. Socure’s login authentication is exceptionally effective because it is a single, end-to-end solution that combines historical device fingerprinting and behavioral analytics to verify users by means of a dynamic risk approach. Instant risk assessments allow low-risk logins to be automatically approved in real time, and higher-risk logins to receive more secure vetting through a screening of email or phone risk prior to sending an OTP, or stepping up to ID and biometric verification. This ensures maximum security.

Usernames and passwords are regularly exposed thanks to phishing attacks, data breaches, and social engineering. With the vast majority of users reusing passwords, the breach of a single website often leads to the exposure of login authentication information for dozens of different accounts. Fraudsters can easily purchase any number of the 24 billion usernames and passwords currently up for sale on dark web marketplaces. The major risk of password compromise underscores the need for a broader and deeper form of digital identity verification to increase security, reduce identity fraud, and maintain customer trust.