Identify good users and flag suspicious ones by analyzing human interactions, device attributes and account activities.
SecuredTouch uses behavioral biometrics and machine learning, to analyze human and device attributes, to automatically identify trusted users and flag suspicious activities, before transactions take place.
SecuredTouch’s technology continuously analyzes 100s of data points generated by the innate physical interactions between a human and a device to create unique user profiles for authentication. Machine learning capabilities ensure that these profiles are as accurate and reliable as possible. Our behavioral biometrics technology was designed to passively identify good users first, in order to ensure a seamless user experience. The removal of cumbersome authentication layers leads to increased conversion rates and boosts customer loyalty.
The interactions of a user with a website or application differ between legitimate users and fraudsters. Fraudsters aim to carry out their attacks as quickly and efficiently as possible so they can move on to their next target. Moving between or filling in fields in a registration form too quickly, or going directly to change the shipping address instead of browsing and adding items to the cart, are just some examples of deviations from normal user behaviors. Recognizing these actions allows our technology to determine the intent of the user and, therefore, fraudulent activities.
Automated scripts, AKA bots, allow fraudsters to scale up their attacks rendering current fraud detection tools obsolete. Multi-dimensional data analyzed by our Behavioral Biometrics technology automatically distinguishes between human and non-human users. Therefore, fraud detection no longer needs to rely on rigid solutions that ultimately allow previously undetected fraud to slip by. Attempts to monetize on stolen credit card details or user credentials are halted immediately.
Similar to how our technology can separate human vs. non-human users, pairing device attributes and behavioral data allows the classification of fraudulent devices, called Emulators. Identifying if a device has been tampered with or spoofed is easy. Emulators leave a footprint on the OS and incomplete critical behavioral data, like mobile attributes missing accelerator or gyroscope data, automatically raises red flags. When a device generates suspicious attributes, there is no need to wait to assess user activities.
SEAMLESS USER EXPERIENCE
Seamlessly analyze different attributes to determine if a user is the legitimate user and a device used is a legitimate device
Analysis is seamless to users, ensuring a smooth customer journey and early detection of advanced fraud.
Machine learning continuously analyzes physical interactions between humans and devices, device attributes and account activities.
ADDRESSING MULTIPLE FRAUD USE CASES IN A SINGLE SOLUTION
As fraudsters adapt to digital channels, SecuredTouch ensures your customers can securely and easily make purchases.
SecuredTouch presents a unified approach to risk, addressing multiple fraud use cases and attack vectors, in a single solution, that is frictionless to users.
- ATTACK VECTORS COVERAGE
- Identity Theft
- Stolen Credentials
- Stolen Credit Cards
- Compromised Devices
- USE CASES
- Advanced Fraud
- New Account Fraud
- Account Takeover
- Passive User Validation
- Strong Customer Authentication