Detecting and Preventing Fraud in Banking with Data Analytics tools like SASAML, Shell Scripting and Data Integration Studio

Abstract

In the rapidly evolving landscape of banking, the need for robust fraud detection and prevention mechanisms has become paramount. This paper explores an innovative approach to enhance the security measures in banking systems by leveraging advanced Data Analytics tools. Specifically, the study focuses on the integration of SASAML (SAS Anti-Money Laundering), Shell Scripting, and Data Integration Studio to create a comprehensive framework for detecting and preventing fraudulent activities.