Machine Learning Techniques for Credit Card Fraud Detection in Cyber Banking Systems: A Comprehensive Review

Abstract

The rapid digitalization of the Indian banking sector has significantly transformed financial service delivery, enhancing convenience, efficiency, and financial inclusion. Online banking, electronic payments, and the widespread use of credit and debit cards have become integral to everyday financial transactions, ranging from fund transfers and bill payments to e-commerce and ticketing services. While these advancements have reduced operational costs for banks and improved accessibility for consumers, they have simultaneously increased exposure to financial fraud. The growth of internet banking and plastic money usage has led to a sharp rise in cyber frauds, including card-not-present frauds, identity theft, phishing, and unauthorized transactions. Credit and debit card transactions, in particular, are highly vulnerable due to their extensive adoption and direct linkage with banking systems. Despite improvements in fraud detection mechanisms and authentication technologies, cybercriminals continue to exploit system vulnerabilities using sophisticated techniques. This study highlights the evolution of digital banking in India, examines the nature and risks of financial transaction frauds—especially those involving credit and debit cards—and emphasizes the need for robust security frameworks, advanced authentication methods, and continuous technological innovation to safeguard digital financial ecosystems.