(Forbes) Machine vision in finance is proving to be beneficial not only to businesses but also to consumers in general. Financial institutions have now integrated advanced technologies in their operations over the years. These technologies have allowed organizations to improve their customer experience and lighten employees’ workloads. The global machine vision market is expected to grow at a CAGR of 7.7% to reach USD 18.24 billion by 2025. There, however, remains a certain level of skepticism amongst the institutes and consumers in the adoption of machine vision in the finance sector.
Applications of machine vision in finance
Machine learning is slowly but surely making inroads in the finance sector with major banks already leveraging its ease-of-use mechanism. It's paving the way for automated processes substituting the current routine and repetitive practices followed by financial establishments.
A simple KYC process currently takes hours, which can be reduced to minutes using machine vision technology. Many major banks have already started capitalizing on machine vision technology to ease this process. For instance, the Banco Bilbao Vizcaya Argentaria (BBVA) bank is using machine vision for KYC verification. Customers can now open an account using a smartphone via a selfie or a video call. This method of digital verification eliminates the need for manual verification. The customer can open an account from the comfort of their home. It proves to be a win-win situation for the banks too, as they can attract more customers and utilize the time and human resources saved to carry out more important tasks.
One of the most important aspects in which the use of machine vision in finance is beneficial is eliminating our dependency on physical currency. Machine vision can help banks in replacing traditional currency as well as credit and debit cards. These are replaced with one-time-use digital codes on clients' smartphones. This process eliminates the need for carrying physical cards, provides better security, and combats fraud. In 2017, Wells Fargo announced that its 13,000 ATMs would work without debit cards, replaced by codes generated on the user's smartphone.
Major tech companies like Apple, Google, and Samsung have already introduced their mobile payment system, which helps get rid of carrying physical cards. They store the user's card information digitally, which is authenticated by biometrics at the point of sale (POS). Although these payment methods don't eliminate our dependency on cards completely for now, the implementation of machine vision in finance can help us achieve a truly digital society in the future.
Benefits of machine vision in finance
Machine vision can help to eliminate paperwork currently done by financial institutions, saving time, resources, and money. It can replace the traditional methods used in the finance sector for services provided with digital processes. The opening of new accounts and customer verification can be done quickly and more efficiently using digital methods. It also helps in improving backend operations, which are currently time-consuming.
A simple task such as opening an account is tedious in today's age with the customer verification process taking days or even a week. This can be reduced to hours or even minutes using machine vision-enabled services.
Machine vision can also help us get closer to the dream of a truly paperless and cashless future, where every transaction is done digitally on a smart device. Devices like mobile phones and smartwatches can be used to carry out the transaction. Further improvement in this technology can bring about to a scenario where transactions can be completed even without digital codes.
Biometric data like an iris scan of the customer can be used to verify the customer and authorize transactions. This can improve security and address privacy concerns as iris scans are difficult to replicate. The instances of bank frauds can reduce significantly, providing a safer and secure environment for the user. This increases the trust of the customer in the organization and may even help attract new customers.
Machine vision can also be used for quick and correct settlement of insurance claims, especially in the automobile market. Using machine vision, one can assess the extent of damage and discern whether the incident was an unfortunate one or done with fraudulent intentions. The use of cameras helps verify claims quickly and accurately. Machine vision could, in some cases, eliminate the need for human inspectors while providing accurate real-time data. Insurance companies can benefit hugely from this as it eliminates the chances of fraud and false claims.
Challenges in implementing machine vision in finance
As beneficial as machine vision may be to the finance sector, it's not without its limitations and challenges. Consumers are skeptical of using machine vision technologies as they still prefer the traditional pen and paper methods for availing services. Consumers are still accustomed to traditional practices as digital services have just started making their way into working practices.
Consumers need to be educated about the benefits of using digital services efficiently without compromising their private data. Adequate information should be passed on by banks informing customers about the do's and don'ts of each advanced digital service.
There are many instances of bank fraud where criminals cheat customers of their money by using their card information. They spam them to get their confidential details. Such instances instill fear in customers considering adopting other digital services or even continued use of their existing ones. Customer data can be compromised and exploited if not stored securely by the banks. Banks need to be one or more steps ahead of the scammers and put stringent practices in place to prevent mishaps.
There is also the cost involved in the research and development of software and hardware which cannot be easily exploited by fraudsters. A lot of time and capital needs to be invested in providing a seamless and secure user experience. Not every financial institution can afford to invest necessary capital and human resources. It's only after the technology becomes affordable that small institutes can provide the services to their customers without affecting their operating costs significantly.
The use of machine vision in finance can significantly help reduce fraud that dupes customers and business of their money. It will create enormous opportunities in the financial industry for banks to gain the trust of their customers while forgoing tedious procedures. It will help banks cut down on their costs and save time as most of the services would be digitized with almost no human intervention. Thus, the use of this technology can do wonders if implemented well, taking into consideration all the advantages and limitations of computer vision.
