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Computer Vision can Revolutionize the Fintech and Payment Industry

November 14. 2022

The Fintech and payment industry are evolving to include more technology in its everyday processes. This is because there is a lot of data to be processed and analyzed. The use of the AIOps platform for IT Operations is now widespread within the industry, and one of the recent AI solutions includes compute vision. Computer vision in this sector helps to observe various types of data and generate actionable insights.

Understanding Computer Vision

Computer vision is a form of artificial intelligence that helps to generate important and insightful data. This data is obtained from images, videos, and various visual sources. It obtains information from inputs that would otherwise not be analyzed and when combined with machine learning and AI-led automation, computer vision can prove to be beneficial across industries.

Computer Vision for the Fintech and Payment Industry

Computer vision plays an important role in the Fintech and payment industry. Following are some of its major benefits.

  • Payment and Banking Security

Payment methods change all the time, and there have been many new ones. But each of these methods utilizes client data which is personal and can be sensitive. Therefore, it is important to always ensure the security of such data. Modern payments are of various types, and most are trying to eliminate the need for a physical credit or debit card. If one does not have to carry a card, the risk of data theft is slightly lower.

Financial institutions like banks are trying to reduce the need of using a debit or credit card to withdraw money. This is possible through the implementation of computer vision. Payments can now be made through smartphones. However, smartphones can also be stolen, and naturally, this is a point of concern. The implementation of computer vision includes various safety measures that prevent theft or loss of sensitive information. Smartphones are equipped with security solutions like biometric authentication. This includes face recognition, a fingerprint scanner, and even an iris scanner. Similarly, AIOps security solutions can be implemented by banks to ensure that all payments are safe. With the widespread use of computer vision, banks can also introduce these solutions in their mobile apps for safe and efficient payments.

  • KYC Verification

Every financial institution requires KYC (Know Your Customer) verification before allowing any payments or payment-related activities. The process verifies the identity of clients and suitability and conducts some risk assessment that helps to understand if it will be possible to enter into a business relationship with a particular client. KYC verification is not only necessary for the safety of the bank’s data and business, but also for the reputation of financial institutions. People are more likely to trust those that conduct KYC verification. This is because the verification ensures all clients, including agents, distributors, and consultants, are anti-bribery compliant.

KYC verification at this point can be time-consuming, especially if much of the analysis is done manually. Human intervention can lead to errors and be quite costly. Computer vision algorithms can automate the entire process. Implementing the best AIOps platforms software solutions along with computer vision will allow clients to have their KYC verification done within minutes and open an account at any time of the day. There will be no delay in payments, and clients can begin to avail services immediately after the verification is complete.

  • Better Investment Strategies

Satellite images are a benefit of implementing computer vision. In the finance industry, the use of satellite images can help with investment strategies. For example, when a company or an individual wants to invest in property, satellite images can provide accurate data. This data will not only be on the current condition of the property but also provide insights into the surroundings and the state of the entire site. Analysis of this data will help companies determine if the property is worth the investment.

Computer vision through satellite imagery can also provide more complex data. For larger investments, for example, if a company is investing in a store in a particularly busy commercial area, satellite imagery can help to determine the consumer count for other stores in the area. This is done by analyzing the number of vehicles on parking lots at any given time. Such analysis is impossible through manual processes. Companies cannot have their employees walking around stores to understand how many people are coming in on any day of the week. But such data analytics is useful if done correctly. Satellite imagery makes it possible and thus, reduces investment risks significantly.

Computer vision algorithms can process data from various sites for multiple investments. Analysis of this data helps companies understand the prospect of their investments. The benefit of using computer vision for investment strategies is that companies get a host of solutions like supply chain monitoring, geospatial intelligence, and real estate due diligence at their fingertips.

  • Analysis of Insurance Requirements

Before providing insurance, there is a need to conduct a thorough analysis of assets. This is a part of risk assessment and helps to determine the suitability of the client. Usually, computer vision is important for property analysis. Combining satellite imagery with other artificial intelligence tools, it is possible to get insights into the state of assets available, estate specifications in case of property analysis, and several other requirements, eliminating the need for physical verification which can be expensive and time-consuming.

The use of computer vision enables insurers to immediately verify assets and enhance underwriting, speeding up the quotation process. AI solutions also allow insurers to monitor assets, especially property. They can keep track of the condition of assets and check if there have been any modifications during the period of insurance.

Using computer vision, AIOps security, and other AI tools, insurance companies can process client data. The analysis of client data is crucial for risk assessment, understanding of loss, asset valuation, and resolution of any problems that may occur in the future. Data can be structured and stored for later use, and when necessary, it can be accessed through the application of machine learning for high-resolution imagery.

Conclusion

The entire Fintech and payment industry is utilizing the AIOps platform for IT operations to move into a more technologically advanced space. The adoption of AIOPS facilitates leveraging the tools like computer vision. Many financial institutions are now implementing computer vision in different aspects of the industry. This is providing a more efficient infrastructure for payments and other financial services.

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