Integrated, synthesized analysis
Unlike traditional fraud solutions that stack layers of telemetry and push fraud alerts from each data source independently, BioCatch’s Continuous Behavioral Sequencing technology uses multiple machine learning engines in parallel to analyze thousands of fraud signals in context.
Session activities, decisions, location, movements, timing, and more are parsed, matched, and coalesced continuously in real time to identify deviations at three distinct levels: user, fraud, and population.
Continuous user validation and recognition
Deviations in a user’s normal behavior consistent with known fraudulent behavioral attributes, such as hesitation, abnormal navigation within an application, the presence of remote access tools, copy and paste activity, typing cadence, an active phone call during a session, and hundreds more are used to evaluate the authenticity of each user during a unique session.
Individuals’ needs and behaviors can change slowly or quickly depending on their purpose, need, or desire. These changes are reflected in our analysis, and based on the contextual data, only BioCatch’s CBS technology can most accurately determine if these changes are legitimate, permanent, or fraudulent and provide highly accurate risk scores specific to that user’s session attributes.
Proven risk modeling
and profile mapping
It used to be easier to determine whether session activity was fraudulent, but today’s reality is quite different. Without the right data and analysis engines, it is nearly impossible to distinguish between genuine and fraudulent activity without having a high rate of false positives.
Our customer-validated and industry-proven fraud and money laundering models, such as Account Opening, Account Takeover, Scams Identification, and Money Laundering Detection, are continuously enhanced and tested against hundreds of billions of unique sessions to refine our risk models and genuine and fraud profiles. These profiles are compared to the ongoing collection of real-time session activity to more accurately determine risk.
With CBS, customer interactions are scrutinized against billions of historical sessions, known genuine and fraud profiles, and correlated against hundreds of different fraud tactics to provide risk scores and predictive intelligence for FRAML response guidance.