Why is identity verification important in gig economy? Click here for a guide on fraud challenges faced by gig industry & how identity verification can help resolve them.
There are many pursuing an at desk, full-time job diligently, juggling their various responsibilities, but the gig economy is also booming. With the promise of a quick buck seeming lucrative, many have taken to driving or delivering for companies such as Uber, Ola, Swiggy etc. In the US alone more than 56.7 million Americans were engaged in the gig economy in 2019 and will probably make up more than half of the labor force by 2024!
What this also means is that in recent years, there’s been a huge volume of financial transactions for these companies. And what happens when there is an overwhelming volume of transactions an organization cannot easily attend to? Some companies then do not prioritize some aspects of transaction monitoring, in few cases fraud checks and management. Identity verification takes a back seat.
What has the dearth of accurate identity verification led to? 48% of employers have had to deal with identity fraud amongst freelancers in the gig economy. The act of impersonating a gig economy worker is on the rise with companies that do not have fraud checks. The fact that they are under a false cover has allowed them to cause financial and personal damage to customers without any fear of consequences.
The good news is that not all companies have let themselves slip when it comes to identity verification. Companies like Uber, Ola, and Swiggy have a very robust and secure identity verification process already in place. With the assistance of HyperVerge, the use of identity verification has also helped companies like Swiggy and Uber get around problems like independent contractors not having business registration numbers at times. In fact, they may have to go in for a driver’s license at the time of registration. With a lack of any kind of documentation, identity verification still helps establish a unique identity for each contractor/gig worker.
The process consists of three steps:
(1) Image Capture
(2) Image Analysis
(3) Image Comparison
In the first step, the facial image is extracted from video or pictures. In the second step, the features of the face are then converted into digital information consisting of data and vectors. This will not only set apart each distinguishing feature of the face but also account for the distances between them. The last step is the process of comparison, that of comparing the face from the image with the one stored in the database (to authenticate a user, for instance). There is a possibility of comparison with “N” other facial samples stored also in order to identify a suspect for de-duplicate or fraud check.
But a simple identity verification of this sort may not be enough for organizations such as Swiggy, Uber, Ola etc. to check for frauds. 2D static attacks have given way to 2D dynamic attacks today. In 2D dynamic attacks, pictures and video are flashed on a 4K screen and holes are used for the eyes. 3D attacks that make use of face masks are another possibility. Fraudsters today carry out a number of face spoofing attacks that can get past an active liveness detection system. Enter passive liveness detection systems, like those from HyperVerge. Not only are they more user-friendly, but they do not require the user to make any movements. They remain accurate and hassle-free.
HyperVerge’s passive liveness detection system is iBeta certified for accuracy and we are tied with #3 on the NIST certification platform. HyperVerge’s AI-backed face recognition system is not only high green channel but it is also 256-bit end-to-end encrypted for secure communication of data at both rest and in motion. The GDPR readiness ensures that it conforms to the user data privacy regulations of the EU. SOC 2 compliance ensures that security is top notch. ISO certification also helps to build further trust with the customer on the continuous quality of the face recognition process.
Several clients and brands trust HyperVerge. Several FinTech enterprises such as Moneytap and FE Credit, the largest consumer lender in Vietnam, have benefited greatly from the face recognition system of HyperVerge. In the telecom sector too, HyperVerge has worked with companies such as Jio and implemented face recognition systems that help strengthen their already quite stringent identity checks. In the gig economy, HyperVerge has already helped companies such as Swiggy, Ola, Uber etc.
HyperVerge continues to push the limit of achievable excellence with continuous benchmarking against the competition to improve the performance of its AI-driven face recognition system. Talk to us to know more about how we can help you with face recognition that can give you peace of mind, fighting fraud as you grow your business.