FRAUD AND ID VERIFICATION
“ We are using biometrics, in a very secure way with modern AI systems”
Eduardo Azanza, CEO, Veridas enabling real-time analysis of security features invisible to human reviewers.
Modern systems examine paper quality, printing patterns, font consistency, and embedded security elements to detect sophisticated forgeries.
These platforms process thousands of document types from over 200 countries, automatically flagging inconsistencies while maintaining processing speeds measured in seconds.
Machine learning algorithms improve detection capabilities by analysing new fraud patterns as they emerge.
Risk scoring based on user behaviour patterns offers promising opportunities in fraud prevention.
These systems analyse keystroke dynamics, mouse movements, device fingerprinting, and geolocation patterns to build comprehensive user profiles.
Continuous authentication monitors behavioural changes throughout sessions, identifying potential account takeover attempts even after successful login.
This approach proves particularly effective against sophisticated attacks where criminals gain legitimate credentials but cannot replicate behavioural patterns.
Effective MFA implementations combine multiple verification methods without creating excessive user friction.
Mobile-first approaches leverage smartphone capabilities including biometric sensors, secure elements and push notifications to create seamless verification experiences.
The challenge lies in balancing security requirements with user experience expectations. Progressive verification strategies adjust authentication requirements based on risk assessment, requesting additional verification only when warranted by suspicious activity. fintechmagazine. com 157