Published on March 27th, 2023
Facial recognition technology has been making waves across various industries, and the BFSI (banking, financial services, and insurance) sector is no exception. It is expected that the adoption of facial recognition technology in BFSI will continue to grow astonishingly in the coming years.
According to a report by MarketsandMarkets, the global facial recognition market is expected to reach $8.5 billion by 2025, with a CAGR of 17.2% from 2023 to 2025.
But what is driving this growth, and what are facial recognition technology use cases in BFSI?
In this article, we will explore the potential of this technology for improving security, reducing fraud, and enhancing the customer experience in the BFSI industry.
How Does Facial Recognition Technology Work In The BFSI Industry?
Creating A More Secure Onboarding Experience
When a customer applies for a new account, facial recognition technology, enabled by AI and machine learning, can be used to verify their identity and authenticate their personal information. Here’s how it helps:
- The customer submits their personal information and a photo of themselves through the bank’s mobile app or website. The facial recognition technology then compares the photo to the customer’s government-issued ID document, such as a driver’s license or passport, to ensure the person is who they claim to be.
This process helps prevent identity fraud and ensures the customer’s information is accurate.
- Facial recognition technology can also authenticate the customer’s identity for future transactions through its datasets. For example, when customers log into their account or make a high-value transaction, they may be asked to take a photo of themselves to confirm their identity. The technology can compare this photo to the customer’s initial photo on file and to other security measures such as their fingerprint or voice recognition.
User authentication helps ensure that the correct person is carrying out the transaction.
Modernizing Mobile Security Solutions
Facial recognition technology is increasingly used in the BFSI industry to modernize mobile security solutions and improve the customer experience. Here are some ways in which facial recognition is being utilized:
- Secure mobile banking: Customers can log in to their mobile banking app using their facial biometrics, eliminating the need to enter passwords or PINs. This makes the process more seamless and secure, as facial recognition technology can prevent unauthorized access to customer accounts.
- Contactless payments: Customers can authorize transactions using their facial biometrics without touching any buttons or screens. This reduces the risk of transmission of germs and viruses and provides a more convenient and secure payment method.
- Anti-fraud measures: With anti-fraud measures, the BFSI industry can prevent unauthorized access to customer accounts. When a customer attempts to log in to their account from a new device or location, facial recognition technology can compare the face with the one on file to ensure that it is the authorized user. This helps prevent identity theft and other fraudulent activities.
- KYC (Know Your Customer) compliance: Banks are required to comply with KYC regulations to prevent any irregularities. Facial recognition technology can verify the identity of customers during the onboarding process and periodically after that. This provides a secure and efficient way to comply with KYC regulations and prevent financial crimes.
Remote transactions are becoming more popular as consumers shift to digital channels. The use of facial recognition technology can help banks and other financial institutions ensure that they comply with Know Your Customer (KYC) norms.
Here are some ways in which facial recognition is being utilized in remote transactions:
- Digital account opening: Customers can submit their personal information and a photo of themselves through the bank’s website or mobile app. The facial recognition technology then verifies their identity through eKYC. This allows customers to open a bank account remotely without visiting a physical branch.
- Digital payments: Customers can make payments using their mobile devices, and face detection can be used to authenticate the transaction. This provides a secure and convenient way to make payments remotely without needing physical contact.
- Remote customer service: Customers can video chat with customer service representatives, and facial recognition technology can be used to verify the customer’s identity. This allows customers to receive support remotely without visiting a bank branch.
- Mobile check deposits: Customers can deposit checks by taking a photo of the check and submitting it through the bank’s mobile app. Facial recognition technology can verify the customer’s identity and prevent fraud.
Facial recognition provides a secure and convenient way for customers to complete transactions without visiting a bank branch physically.
Enhancing ATM Convenience And Security
ATMs are under increasing pressure from cybercrime and fraud, especially in the BFSI sector. According to a recent report from KPMG, financial institutions have seen increased cyberattacks against ATMs.
The financial impact of these attacks is significant; costs incurred by the industry include not only the direct costs of fraudulent transactions. It also includes indirect costs such as revenue loss due to customers’ fear of using ATMs.
Now, how does facial recognition counter this threat?
- Facial recognition technology is used for cardless ATM withdrawals, providing a convenient and secure way to withdraw cash without a physical card.
- The technology is also utilized to prevent ATM skimming, identify suspicious activity and ensure customer data security.
- With facial recognition technology, personalized ATM experiences are possible, recognizing customers and providing tailored greetings and offers.
- The facial biometric verification can enhance the security of ATM transactions, providing an additional layer of authentication beyond PINs and passwords.
Creating Secure Customer Service
Facial recognition technology provides a more advanced and reliable method of authentication. With facial biometrics, customers can be identified accurately, which helps:
- Reduce the risk of fraud;
- Ensure the security of their accounts;
- Eliminate the need for physical identification documents, such as IDs or passports, which can be lost or stolen.
The technology can also detect suspicious activity and alert security personnel, preventing unauthorized access to customer accounts.
How To Collect Data For Facial Recognition Model Training
There are several ways to collect facial recognition model training data. One of the most common ways is to gather images from publicly available sources, such as social media platforms, news articles, and online image data. However, this method can be limited in terms of the quality and diversity of the data, as well as potential privacy concerns.
Another approach is to collect data directly from individuals through various means, such as consent forms, surveys, and questionnaires. This can provide more diverse and high-quality data, but it requires more resources and time to coordinate.
Data collection services can also be used to obtain facial recognition training data. These services specialize in collecting and annotating large datasets of images, which can be tailored to specific needs and requirements.
Regardless of the approach, it is essential to prioritize data privacy, and ethical considerations throughout the data collection process, such as obtaining informed consent, protecting sensitive information, and ensuring the data is not biased or discriminatory towards any groups.
The changes are ongoing, leading to a more bankable, profitable future that provides a better user experience. With these changes coupled with the ability to learn from other companies’ mistakes, the BFSI sector will continue moving forward rapidly towards using facial recognition—a more effective, safer end goal for all bodies involved.
Vatsal Ghiya is a serial entrepreneur with more than 20 years of experience in healthcare AI software and services. He is the CEO and co-founder of Shaip, which enables the on-demand scaling of our platform, processes, and people for companies with the most demanding machine learning and artificial intelligence initiatives.
Image Source: commons.wikimedia.org