The cybercrime prevention solution provider’s new product is a software development kit (SDK) designed to identify fraudulent transactions originating from mobile applications (view press release). The new solution is a continuation of ThreatMetrix’s malware detection and cookieless device identification technology for computers, providing the same fraud screening capability for mobile as used for browser based transactions.
The solution is an embeddable library for smartphones that creates cross-validating device fingerprints based on hardware, operating system and application parameters. Businesses can embed the solution into company smartphones and activate a comprehensive fraud screening solution covering both browser and mobile application transactions.
Alisdair Faulkner, chief products officer at ThreatMetrix says the new solution “ensures that trusted user device identification and reputation is tightly integrated into a single platform for reducing risk across all web transactions and applications.”
The solution is currently available on the iOS operating system. It is in Beta for other devices and due to launch later this year.
Whitepapers
Related reading
Central banks best suited to issue digital currencies
By Aaran Fronda A recent report by the Official Monetary and Financial Institutions Forum (OMFIF) said that central banks rather than private ... read more
Instant payments: innovations inbound for corporates
In 2020, instant payments look set to continue their current trajectory to become the biggest trend in payments. While these schemes already offer numerous benefits to corporates, leveraging innovations such as APIs and request to pay will go some way to unlocking their full potential, argues Michael Knetsch
Obstacles exist for banks to meet ECB’s instant payments goal
The cost of joining instant payment platforms will be one of many hurdles banks and payment services providers must overcome to meet ... read more
Banks must be aware of “biases” in data used to train ML models
Financial institutions need to be conscious of biases in the historical data that is being used to train machine learning (ML) models, ... read more