The financial sector is, undoubtedly, the engine that powers the world economy. Creation, management, growth, and distribution of wealth are paramount to economic progress, and financial institutions play a profound role in facilitating these elements. This also means that the financial sector should overcome inefficiencies, errors, and delays at all times to ensure a smooth and stable customer experience. This is one reason financial institutions are also one of the forerunners in investments in technology to design a highly flexible financial ecosystem that can quickly adapt to market trends and serve consumers better.
For a long time, the financial sector has seen astonishing progress in adopting emerging technologies like data analytics, cloud computing, mobility, and artificial intelligence. But today, digital experiences demanded by customers are so elevated that financial organizations must venture into new territories in their digital ecosystem. One of the most prominent enablers in this expanded scenario is computer vision.
In simple terms, computer vision refers to the use of cameras and machine vision to acquire, process, and deliver insights from visual representations like an image or videos. It helps understand the context behind different visual patterns and finds great use in several sectors. Today let us explore how the financial sector can leverage computer vision to build better customer offerings:
Lower Document Processing Effort
Banks and financial institutions are places where there’s too much documentation to be handled by staff at any given time. Be it account opening forms, loan applications or credit statements, or transactional information, there are thousands of documents having precious customer data captured in them.
Computer vision can help in the automatic extraction of relevant data from documents. In simple terms, it uses image recognition algorithms to clearly understand and extract contextual data from documents to supply it to relevant stakeholders. This helps staff to focus on more productive and valuable tasks instead of manually capturing data from each document, which can take several days.
Securing ATM Transactions
Computer vision can make ATMs a safer place for customers to withdraw cash. Rather than just relying on the ATM card and a pin to unlock it, the withdrawal request could be made via biometrics, wherein motion cameras can detect customers from live video feeds from within the ATM once they have entered.
User privileges can be authenticated by facial recognition or scanning of the iris, which again throws light into the use of computer vision for accurate validations. It can also be configured to detect any suspicious behavior within the ATM, thereby securing users at all times.
Insurance Claims Processing
What if an automobile crash insurance claim can be fully processed digitally? Sounds too good to be true, right? Well, computer vision-enabled claims processing makes this a reality. By analyzing photos and videos of the damages in the vehicle, insurance systems can automatically process claim requests and certify the right coverage to be issued for the damage.
The best part about this is that it eliminates manual oversight, enabling thousands of similar claims to be processed simultaneously, leading to faster decisions. This profoundly impacts customer sentiments as a faster resolution is often the number one trait that customers look for when selecting an insurance provider.
A similar example would be healthcare reports. Using AI computer vision, hospital reports can be easily authenticated by insurance companies to decide on claim settlement requests faster. It also brings a lot of efficiency to the process by eliminating tons of manual work usually involved.
Virtual KYC
As financial institutions focus on improving their customer onboarding and validation initiatives, computer vision can help bring a new perspective to these processes. A combination of facial recognition, document data extraction, biometrics, and intelligent data analytics can help banks and financial organizations onboard new customers automatically without the need for them to ever visit a branch.
Computer vision aided systems can automatically extract relevant data from supporting documents and even help in opening a bank account from literally anywhere.
The Need for a Platform Approach
As we can see, there are different use cases for computer vision in the financial sector. Multiple departments within a financial organization can use it to streamline their operations. For example, computer vision can work with CRM to onboard customer information through document extraction, empower faster claim processing with insurance systems, settle invoices, bills, and other accounting documents through visual processing, and much more.
The underlying fact here is that there needs to be a way for computer vision to be an integral part of all operational streams so that it can be freely leveraged on demand for various objectives.
This is where a platform approach for computer vision makes sense. KamerAI understands the power of such a platform that can bring intelligent decision-making through visual analytics to nearly any business.
Get in touch with us to learn more about how to incorporate visual analytics into your technology stream for better ROI.
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