HID Global, a U.S. manufacturer of secure identity solutions, has introduced a new cyber-threat and fraud detection service that brings artificial intelligence (AI) and machine learning to identity and access management, addressing the rising incidents of cyber-attacks, especially against financial institutions.

The new HID Risk Management Solution is a real-time risk profiling technology that uses data analytics to protect financial transaction systems and banking applications against cybercriminals. 

The ever-changing threat landscape that makes it more challenging to secure online and mobile financial applications has created an urgent need to improve the detection of fraud and cyber-attacks and accelerate the prevention of costly damage. The new HID Risk Management Solution meets this need by combining evidence-based detection capabilities and behavioral biometrics, supported by machine learning.  This HID cyber-security solution is designed to protect banks, online merchants and service providers against zero-day malware, account takeover, phishing attacks and bots, among other threats.

Adding trust to online transactions and preserving the integrity of digital business interactions, the HID Risk Management Solution is fully integrated with HID’s ActivID Authentication Platform, which provides multi-factor authentication to protect users’ IDs, transactions, devices and accounts.

The merging of multi-factor authentication with the new level of AI-driven, cyber threat and fraud detection is a strategic advancement that delivers risk-based, frictionless and adaptive authentication based on data intelligence. It increases security while optimizing the user experience as seamless, without any inconvenience to consumers.

The HID Risk Management Solution is composed of three different risk-based engines that are fully integrated together and deliver a more accurate risk score than other systems from other vendors. Those three risk-based engines are:

Threat engine – detects generic threats and tracks device IDs, identifying application integrity tampering, malware and phishing attacks.

Behavior engine – creates a behavioral biometric profile of the user, including keyboard, mouse and swipe behavior, page navigation and application usage.

Anomaly engine – monitors the details of transactions in real-time, utilizing machine learning methods to identify anomalous sessions and transactions, as well as continuous analysis of hundreds of parameters. 

The risk score that is generated from these three “engines” enables more informed decision-making about the next action, whether to approve, block or reject a transaction.  The data analytics from the IAM solution creates new value for customers. 

Topics