Ai And Big Data In Digital Payments: A Comprehensive Model For Secure Biometric Authentication

Abstract

Biometric data, including keystroke dynamics and voice data, is being employed by banks to authenticate the identity of users making digital payments. This data is being analyzed using artificial intelligence tools to detect any fraud attempts. The biometric data of users making online transactions is compared to their previous data collected when they used mobile banking, internet banking, and other banking services. If this biometric data is found to fluctuate beyond a certain threshold, the transaction is flagged as a possible fraud. This mechanism is proving beneficial as a large number of fraud attempts are being detected in the early stages before losses amounting to crores of rupees occur. Big data, a very large set of information that is complex and difficult to manage, is being used to aid a machine learning-based fraud detection system in making predictions. Looking at the pattern of transactions already made alongside the claims of fraud against these transactions allows for past transactions to be classified as any of the two classes of predicting fraud: ‘fraud’ and ‘not fraud’. Based on these credible past transactions, future transactions can also be predicted, and frauds attempted can be classified as ‘true positive’, ‘false positive’, ‘true negative’, or ‘false negative’. Also, multiple decisions concerning the model of classifying fraud can be considered simultaneously if big data is employed in the machine learning model. This technology is extremely useful for banks to detect fraud early in the transaction process.