Big Data-Driven Fraud Detection in Digital Banking
Keywords:
Big Data, Data Mining, Big Data Mining, Data Security, Security Activities, Cloud Environment, Banking Sector, Banking Services, Cloud Computing, Cloud Users, Fraud Detection, Fraud Services, Credit Card Fraud Detection, Security Breaches, Real Data Analysis, Data Analysis, Credit Card Fraud, Detection Methods, Detection Techniques, Detection Services.Abstract
Fraud detection has been at the forefront of technology and research for decades, focusing on the right techniques and tools for accurate detection in a digital banking system. With the introduction of big data in various domains, its technologies and tools lend eyes and ears to businesses, ensuring rapid and accurate decision-making. The financial domain has evolved immensely with the introduction of cards, mobile banking, and internet banking; security has also become crucial for banks and their customers. Fraud detection and prevention in the banking environment is a costly affair, leading to financial losses for banking sectors, retailers, and the customers themselves.
Members of the fraud detection and prevention team work under increasing pressure to act blazily and investigate major frauds. To meet these demands, state-of-the-art and new technologies have been explored. The use of big data analytics techniques and algorithms on the can detect fraud effectively and help in fraud prevention. When applied to a banking environment, red-flags are generated in an ethical manner, providing the necessary information to the fraud detection team so they can act decisively regarding standing transactions. The use of Big Data Analytics can help detect fraud early in the banking process.
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