14th July 2023
One of the ongoing challenges for a travel company as they combat fraud is to ascertain the legitimacy of a travel shopper – whether they actually are who they say or not. The objective is to flag potential fraudulent transactions based on real-time data by identifying shoppers across devices, networks, and identities. Device risk is an integral part of risk assessment.
Device risk assessment
Airlines that have been running their respective businesses for a fairly long time have to review transactions with high device-risk scores and make speedy assessment of the same. One needs to evaluate risk constantly through an entire user session in order to augment risk scoring accuracy.
The goal shouldn’t be just about preventing fraudsters moving in or accessing the system, but also focus aspects like quantitative data that can be relied to modify the rules engine in real-time to act in response to emerging threats. The adjustment can be slackened or tightened whenever deemed right, and also take care of unwarranted customer friction. This way the device risk service along with advanced analytics helps an entity prepare better fraud detection and action. Another major asset now is – relying on machine learning to identify patterns of behaviour across devices, users, networks, and transactional signals.
Some of the device risk indicators include app cloners, emulators, VPNs etc. When one or more of these is being used by a certain device, they will be flagged as a risk indicator.
In case of tampered e-wallet app, a fraudster can adjust its source code to avoid authentication controls and steal user data.
By Ritesh Gupta, Ai Events
