AI-Driven Fraud Detection Systems: Enhancing Security in Card-Based Transactions Using Real-Time Analytics

Abstract

Card-based transactions are susceptible to security threats. Financial institutions monitor and analyze card transaction data to promptly detect and prevent security breaches. The study aims to understand how AI technologies complement traditional fraud detection and transactional analytical systems in combating security breaches in e-banking. Data suggest that 96% of consumer respondents find AI technologies valuable and instrumental for securing and preventing online payment systems from fraud. The study adopts a secondary data-based methodology and undertakes content-based and descriptive analysis to complement the rationale and build its case with some transient inputs from the primary qualitative method via panel interviews.

Existing technologies need the identification of standard operating attributes of genuine card usage and its associated patterns. However, when the consumer trend, followed by the machine learning data pattern, is purely based on descriptive analytical attributes, the identification of a breach or fraud on a real-time basis becomes weaker. The study finds that real-time analytics, involving AI, is a possible solution to bridge this conjecture. AI could use machine learning technologies based on deep learning and pattern recognition to understand card usage beyond the structured limits and transaction patterns set as per the defined algorithm. Down the line, AI-driven fraud detection systems would actually learn and help employers identify fraud with low to zero involvement of the analyst. The study also finds that consumers and financial institutions or credit card companies spend approximately 5 to 6 hours and 8 to 9 hours, respectively, in locating fraud post-identification.