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The market share of Computer Vision to grow by US$16,794 million, by 2025, with an expected CAGR of 25.40% between 2019-2024.
Computer Vision is a technology that has been leveraged by almost all sectors over the years. Discovered in the early 1970s, the first breakthrough in the advancement of Computer Vision came with the advent of Optical Character Recognition (OCR), to extract data from the scanned images. It assists computer or any other devices for analyzing images, videos and other visual inputs. For example, computer vision is utilized by facial recognition in mobile applications. It is also deployed in the image display of social networking sites. Facebook leverages computer vision to improve the quality of images and videos. One of the renowned applications of computer application is its integration in Tesla's self-driving cars.
Analytics Insight has predicted the market share of Computer Vision to grow by US$16,794 million, by 2025, with an expected CAGR of 25.40% between 2019-2024.
That's why researchers are hopeful to utilize this technology in the Banking and Finance Services and Insurance (BFSI) industry for mitigating fraud, increase cybersecurity, customer experience, sentiment analysis, and back and front office processing.
The researchers in the Tata Consultancy Services (TCS) have published a paper "Computer Vision: A Game Changer for Banking, Financial Services and Insurance" explores the possibility for integrating Computer Vision in BFSI. In this article, we will observe the key areas of BFSI impacted due to computer vision.
Over the past few years, the global banking sector has been grappling with an increase in frauds and scams. Despite the stringent measures deployed, BFSI has continued to be threatened by fraud. In India, over the last five years, three major scams have disrupted the operations in the sector, leading to customers losing their faith in the banks. By deploying computer vision techniques like facial recognition and biometric authentication, banks can improve their internal security, and mitigate scams and frauds.
The key challenge that is faced by the banks is the identification of fraudster or scammer in the early stages of money transaction. To counter this challenge, computer vision can be leveraged for enhancing Know Your Customer (KYC) service, so that they can effectively identify and match the users' profile in the situation of adverse media reporting and negative information on the web and social media.
Another challenge that the BFSI industry gets acquainted with is performing time-consuming and mundane tasks. These tasks include scanning through the documents, segregating customers on the basis of their loan and insurance efficiency, and storing the necessary information in manual format. The paper states that computer vision can also be deployed for automating the document classification, and data extraction process for enhanced, efficient and accurate operations. This will also help the employees to focus on tasks that need immediate attention.
Improved customer relation involves understanding the behaviour and requirement of the customer. This is pivotal in any banking organization. Thus computer vision can be leveraged for delivering personalised and contextual services to the customers. With the help of facial recognition technique, the banking sector can analyze the customer sentiments by interacting with the customers. This will provide insight into the pattern of customer behaviour so that strategy can be formulated for improving the customer services and experiences.
Moreover, computer vision can be utilized in keeping an image back-up, in case of fraudulent activities such as card skimming, theft and so on.
Commercial banks are utilizing the traditional OCR software for reading the documents, scanning documents, and extracting data from the scanned documents. But the existing OCR software requires training, in large configurations of data. Any deviation in the template of the entered data would mean that the output is compromised. This software is hence observed to be time-consuming while delivering the compromised output. The paper states that by deploying computer vision technology, the unstructured documents can be read in different templates, thus delivering a promising result of accurate data classification, efficient data extraction and improved data processing.
Scanning the insurance papers to identify the ideal and immediate candidate for insurance is a tasking process. Not only this, but it involves huge documentation and paperwork to fulfil the protocol of granting insurance. It also requires physical verification of the assets for which the insurance is being provided. To make these manual tasks easier, computer vision can be leveraged, for insurance companies to remotely analyze the images of properties and assets the insurance is given for. Computer vision can also help in providing insights about the insurance documents, thus segregating premium members from that of others.
The BFSI sector has a positive outlook on computer vision. However, the paper points out that the adoption of computer vision will have some challenges. The paper states that as data generation has been observed to increase tremendously over the past few years, computing powers are required to analyse such huge amount of data.
The researchers of the paper believe that the banks and insurers must explore the option of deploying computer vision in their operations.
