Why Aadhaar Masking
KYC (Know Your Customer) institutions are required to verify the identity of their customers to prevent fraud, money laundering, and other financial crimes. Aadhaar masking is an essential practice that KYC institutions can adopt to protect their customers' privacy and prevent identity theft.Aadhaar masking is an essential practice that KYC institutions can adopt to protect their customers' privacy and prevent identity theft. KYC institutions can use Aadhaar masking during the KYC verification process, loan application, account opening, and document sharing. By doing so, they can ensure that their customers' personal information is kept safe and secure.
KYC (Know Your Customer) institutions shall use Aadhaar masking in several ways:
•KYC Verification: KYC Verification: KYC institutions can use Aadhaar masking during the KYC verification process. Instead of displaying the entire Aadhaar number, only the last four digits can be shown, which is sufficient for verification purposes.
•Loan Application: KYC institutions can mask the Aadhaar number when a customer applies for a loan. This ensures that the customer's Aadhaar number is not visible to unauthorized parties.
•Account Opening: KYC institutions can use Aadhaar masking when opening an account for a customer. The masked Aadhaar card will only display relevant information, such as the name and date of birth, while keeping the Aadhaar number hidden.
•Document Sharing: KYC institutions can mask the Aadhaar number when a customer shares their Aadhaar card as proof of identity or address.
Why choose us as a Tech Partner
Our Deep learning algorithm for automated Aadhaar masking, to automatically redact certain parts of an Aadhaar card, such as the Aadhaar number.However, there are several challenges that we addressed to effectively implement deep learning-based Aadhaar masking solutions:
•Data Quality: The quality and quantity of the training data used to develop deep learning algorithms are crucial for the success of the system. In the case of Aadhaar masking, there may be variations in the quality of Aadhaar card images and variations in the way the Aadhaar numbers are printed, which can affect the accuracy of the deep learning model.
•Robustness: The deep learning model used for Aadhaar masking is robust enough to handle variations in Aadhaar card images, including image resolution, brightness, and contrast. The model also be able to handle different languages and fonts, as Aadhaar cards are printed in multiple languages.
•Privacy and Security: The use of Aadhaar data for training deep learning models raises concerns about privacy and security.We process masking on AWS secure envirnment in Base64 format, and that the data is not misused.
•Ensure Accuracy: Does't matter how the customer data quality is, our masking solutions is great to identify Aadhar number and mask the first 8 digit number. Our application is capable to handle multi-IDs printed along with Aadhaar, or any rotation.
•Process scalability: Handle an increasing workload or demand without sacrificing performance or quality. In the context of Aadhaar masking, process scalability refers to the ability to handle a large volume of Aadhaar card images and perform the masking process in a timely and efficient manner. Load Balancing and Parallel Processing are used to process multiple Aadhaar card images simultaneously, which can significantly increase the speed of the masking process.
