Improving Fraud Detection in Banking Systems: RPA and Advanced Analytics Strategies

Abstract

The financial landscape is witnessing a dynamic shift, necessitating robust measures to combat evolving threats in the realm of fraud. This research paper delves into the synergy of Robotic Process Automation (RPA) and advanced analytics strategies to fortify fraud detection in banking systems. Recognizing the intricate nature of contemporary fraudulent activities, the study explores the integration of RPA as an automation tool alongside sophisticated analytics techniques for enhanced vigilance. The paper investigates the potential of machine learning algorithms, anomaly detection, and predictive modeling to fortify fraud detection capabilities. Through empirical analysis and case studies, the research aims to unveil the efficacy of this integrated approach in not only identifying known fraud patterns but also in proactively anticipating and mitigating emerging threats. The findings contribute valuable insights into the transformative impact of RPA and advanced analytics on the resilience of banking systems against the ever-evolving landscape of financial fraud. This research serves as a guide for financial institutions seeking innovative and effective strategies to bolster their fraud detection mechanisms in the digital era.