Want to know about the hidden cost of low-accuracy AI? Check this article for full information on low accuracy of AI & how HyperVerge can help to overcome this problem!
The accuracy of AI systems has vastly improved since 2020. However, many AI systems still use outdated libraries and services. And some of these are open source. Such outdated libraries and services can lead to a dip in accuracy. are several options for face recognition and OCR, but it can compromise heavily on accuracy, depending on how robust and well maintained the libraries are.
In any industry such as FinTech, Crypto, gaming etc., onboarding is a crucial step that you cannot do without; every user must go through it. And this includes several aspects of face recognition here such as Liveness check and face matching that cannot be bypassed. And because every user must go through such verification, it can even affect user retention depending on how user-friendly it is. AI/ML code helps arrive at an accurate decision faster. As far as a business is concerned,
AI and its level of accuracy is the difference in onboarding a few hundred or many thousands in the same period. For a smaller business handling a couple of requests a minute, it may not make a difference but as you scale and handle hundreds of requests a minute at times, the speed to accuracy becomes a critical factor. Another aspect of low accuracy face recognition systems that use AI is randomly accepting fraudsters or rejecting genuine customers. This is why high accuracy AI engines matter; to let the right ones in, and the bad ones at bay.
AI systems are not free of racial bias. This is a problem that hinders the usage of face recognition even by government institutions, who actively need some form of face recognition. The degree of racial bias in face recognition depends on the AI and the face recognition engine itself, apart from other factors such as lighting, internet connectivity, and image processing. Speaking of racial bias and its implications, let us consider the case of Microsoft’s face recognition software Face API. Unions in the UK claimed that it does not accurately identify black drivers on the Uber app. Following misidentification, several black drivers even had their accounts deactivated. Because of misidentification by inaccurate AI, companies and governments are reinspecting whether face recognition must be used.
There is less debate on whether OCR must be used. Many organizations, particularly real estate, legal firms and FinTech are now benefiting greatly from it. But to have great OCR, the AI model must be very well trained. Now let us look at how OCR is hampered by poor AI. OCR without AI or backed by AI with low accuracy may not be able to determine the structure of a document. OCR may not work properly, and the risk of duplicates in the system (if cross verification is not done) goes up. Image rectification may need to be done, which requires good machine learning capabilities before OCR can happen. You may be tempted to try generic OCR engines such as Google/Amazon. However, they are not trained for specific documents, and have lower accuracy. They work great for generic use cases, not for specific use cases.
There are several reasons why there is a rise in cost due to low accuracy of AI. Some of them are mentioned below:
Many organizations, big and small, go through this dilemma of build vs. buy. They start with a thought if it is possible to build a face recognition system in-house. But what they underestimate at times is the difficulty in finding good AI engineers. And even if they do, developing those models is a tough task. Not to mention, finding valid sample data for processing and later updating AI models to combat evolving frauds and to support different types of documents. You will need a separate product team to handle all the resources related to it. And what happens when you buy? Companies can focus on their core business, and let an expert take care of the AI models and face recognition. This is the case, unless building computer vision and capable image processing (using AI technology) serves as a strategic differentiator to their core business. Having an organization with the right expertise partnering will help drive more ROI as you scale.
Now that you have decided to buy. How do you make sure that you are going in for the right service provider for your face recognition feature? These are some of the things you can do:
HyperVerge face recognition is powered by an AI with industry leading precision and recall. There are several reasons why HyperVerge is leading the pack.
When you pick face recognition software, do not compromise on the AI accuracy levels or be misinformed as to how well it performs. Without accurate AI/ML, you cannot expect consistent results in face recognition. Talk to us today to take your business to the next level with HyperVerge AI-powered face recognition and OCR.