Computer vision has taken its fair share of time to get out of the researcher’s closet and enter the industrial arena. Training machines to emulate the animal visual cortex is a daunting proposition, translated into reality today. There have been failures; scientists have fooled image recognition engines with various simple tricks; enterprise owners have been hard pressed to find a real business case for computer vision applications.
But that is all in the past now. Thanks to the incredible amount of visual data generated today (95 million photos and videos are shared every day on Instagram alone, 500 hours of video is uploaded on YouTube every minute), along with the innovations in convolutional neural networks and recurrent neural networks, computer vision has come up as a viable and reliable solution for many industries. The power of artificial intelligence as portrayed in text analysis and speech recognition gets a solid boost through the inclusion of computer vision. It increases the capability of extracting value from unstructured data by many folds.
Application of computer vision in the financial service sector
The banking and financial service industry has been one of the pioneering patrons of advanced analytics technology. Their adoption of computer vision for reasons like customer satisfaction and cybersecurity does not come as a surprise.
Security and fraud prevention
The Spanish financial group CaixaBank lets their customers use facial recognition at ATMs to draw cash. The technological and operational excellence needed to drive this sort of a system is incredible but evidently achievable. Many companies from the BFSI sectors have appropriated modes of facial recognition and biometric authentication to prevent fraud, improve customer experience, and enhance security.
Enabling smarter KYC
Banks can process images of customers and match them against millions of images to locate adverse media reports, or items from the social media. This makes the know your customer process comprehensive and gives financial institutes insights of great value. Computer vision can also enhance processes like document classification in terms of speed and accuracy.
Smarter in-branch service
If a bank can run sentiment analysis based on a customer’s facial expression when she enters the branch, it can help the in-house operative muster the right attitude to ensure customer satisfaction. It may also be possible to analyze the customer’s physical appearance and swagger to make near accurate presumptions about her cause of visit thereby enabling the operatives to be proactive.
Computer vision for insurers
Computer vision applications can ease up claim processing for insurance companies as well as the customers. Take a car damage claim for instance. After the car has suffered damage, the claimant can feed images of the damaged parts to the AI driven application. The application can assess the damage and evaluate the claim amount. If the customer is satisfied with the evaluation, he can submit the claim and the insurance company can view the results of automatic assessment remotely and mobilize the amount. The same applies for insurance claims related to property damage.
Retailers leveraging computer vision
Almost every major retail outlet features security cameras. The feed from these can be run through computer vision algorithms to identify repeat customers. They can be targeted through online outreaches with discount coupons and offers to increase their loyalty.
Many brick-and-mortar stores also have an online front nowadays. Computer vision applications can be used to help customers with reverse image search. Suppose a customer sees a product that she wants, she can just take a picture, upload it in the store’s application and the algorithm recognizes it provides the customer with all relevant information about it.
The autonomous vehicles
Tesla’s autopilot version 1 reported a 40% reduction in road accidents. The version two is currently running and they are pretty sure that it will show 200-300% improvement upon the previous version. One of the principal elements responsible for the autopilot is computer vision that accurately identifies objects and prompts reactions. This along with many more sensors create the ultimate marvel of artificial intelligence of this decade.
Tesla is one of the competitors in the race for autonomy and general artificial intelligence along with Google and Amazon, among others.
Computer vision in public health
This is probably one of the most important general use cases of computer vision. The ability to process and analyze complex images automatically creates great new possibilities for the field of medical science, especially in radiology. Scientists have already used image recognition for an early detection of diabetic retinopathy. Strong strides are being made towards large scale analysis of x-Ray and ultrasound images.
Computer vision applications are being actively used for crowd management in the view of the ongoing pandemic. There are applications that sound an alert whenever an area is too crowded or whenever it detects someone not wearing a mask in the premises.
The area as well as the capabilities of computer vision is ever expansive. The market for computer vision is projected to reach $ 21.4 billion by 2024 and that is not a surprising figure given the widespread adoption of the technology.
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Also Read: 5 latest computer vision technologies that will spark off breakthrough research in 2021