World is in the midst of the Artificial Intelligence revolution. Technology has demonstrated its power across various industries like oil and gas, automobile, banking, healthcare, travel, and finance.

One of the several fields rapidly evolving under the umbrella of AI in Computer Vision. It is one of the most compelling types of AI that replicates parts of the human visual system and identifies the objects in images and videos just like humans do. It is everywhere, and nearly all of us have experienced it with or without realizing it. Here is an interesting read on what computer vision really is because we are going to talk about the business value it can provide to enterprises in 2021.

The advances in technology over time have completely changed what we thought would be possible with computer vision. It has outsmarted human capabilities in aspects of labeling and identifying objects. The advances in AI and progress in deep learning and neural networks have made that possible.

The computing power required to analyze tremendous amounts of data is also available now. With new hardware and algorithms, the accuracy rate for object identification is much better now. In less than a decade, this rate has improved from 50% to 90%.

Even with modern technological advancements, we still know very little about how the human brain actually works, but we know enough to make robust algorithms that mimic somewhat close to human behaviors

Neural networks are one example.

Neural networks (the one we use in computer vision) are supposed to mimic the human brain.

Pattern recognition is how Computer Vision attempts to work like the human brain. But that needs lots of data. So, in computer vision, we use data to produce the power of identifying objects like humans.

The huge amount of data generated in the world every day is crucial in computer vision. More than three billion images are shared online every day. The technology uses data to train and improve itself. Feed it thousands of images that need to be labeled, and it will spot the patterns that relate to those labels.

The discipline can be leveraged in multiple industries to achieve enhanced customer delight and satisfaction. For example, retail, insurance, manufacturing, etc.

It can even resolve old challenges hindering the progress of the BFSI sector. It can help in customer experience management, sentiment analysis, customer identification and authentication, and cybersecurity.

The adoption of Computer Vision can create exponential value in several use cases across retail banking, commercial banking, and insurance.

Read more: Benefits of Sentiment Analysis for Businesses

Computer Vision can help improve KYC (Know Your Customer Onboarding). Banks can effectively search and match customer pictures. It helps identify adverse media reporting and negative information on the web and social media.

There are a lot of tasks in KYC that require manual effort and are paper-based. For example, onboarding, mortgage, credit card initiation, and loan origination. Manual work is required to scan, store, classify, and extract data.

That can disturb the efficiency. Both efficiency and accuracy can be achieved through computer vision. The technology automates document classification and data extraction processes.

A lot of areas of in-branch experience can be improved via implementing computer vision. One good example is the customer identification process. The camera positioned at the entry point can take the picture as the customer walks in, and systems can compare it with pictures fed in.

It is even possible to predict the reason behind a customer visit, thanks to insights that analytics generate. This way, you can proactively deliver the desired service. Moreover, any opportunities to cross-sell or up-sell can also be identified.

Facial recognition also includes analyzing customer sentiments with the help of facial expressions. That can help improve service. Computer Vision systems can identify any different potential scams like theft, card skimming, etc.

To automate document classification and data extraction, commercial banks have been traditionally using OCR software. The existing system works on the rules and templates. The training process is extensive as every variation has to be trained and configured afresh.

Even a slight deviation can cause huge inaccuracy in data extraction. More than 70% accuracy is still not possible, even for multinational banks after years.

The OCR tools based on Computer Vision can ‘read’ unstructured documents in varying templates. They can automate document classification, data extraction. Better processing increases the accuracy levels.

Some examples are Amazon Rekognition, Google’s Vision API, and Azure Computer Vision.

In property insurance, computer vision can significantly decrease the need for physical inspection of properties. Insurance companies remotely analyze images of properties to access granular details such as the condition of the exterior.

Here are some of the integral computer vision solutions that businesses can leverage.

Advances in health technologies also have some dependence on computer vision. We have to detect cancerous moles in skin images or find symptoms in x-ray and MRI scans. Computer Vision algorithms can help us automate such tasks.

Computer Vision can better ensure the safety of the public at large and crowded events. The potential issues with overcrowding can be detected at early stages.

Computing devices such as smartphones, tablets, and smart glasses can overlay and embed virtual objects on real-world imagery with the help of Augmented Reality technology or abbreviated as AR.

Computer vision algorithms play a crucial role in AR. AR detects the objects in the real world to determine the locations on a device’s display to place a virtual object using computer vision.

AR applications can detect planes like tabletops, walls, and floors with the help of Computer Vision algorithms. This helps establish depth and dimensions and place virtual objects in the physical world.

Self-driving cars can understand their surroundings with the help of computer vision. Here is how it goes.

The cameras shoot videos from different angles. Computer vision software analyzes this data and processes the images. Hence it figures out different critical parameters like extremities of roads, reads traffic signs, and detects other cars, objects, and pedestrians.

Often customers want to buy something but do not have ample information about it. Object Recognition technology can provide a helping hand here.

It can recognize such products and provide required contextual information. The technology can direct the user to the same product.

Computer Vision can help estimate the claim amount. The algorithms auto-detects the damaged parts and auto-assess the severity of the damage.

Human eyes cannot detect surface defects on steel sheets. It takes high-frequency cameras.

In a nutshell, Computer Vision has now demonstrated its power in several industries. The market size is expected to reach $19.1 Billion By 2027. Moreover, due to open source technologies and open data sources, it is also not as expensive as it was years ago. It is time to delegate routine business processes to machines and set the staff free for more productive tasks.