RPA and AI-Driven Predictive Analytics in Banking for Fraud Detection

Abstract

This research paper explores the integration of Robotic Process Automation (RPA) and Artificial Intelligence (AI)-driven predictive analytics in banking for fraud detection. In the face of increasing digital transactions and sophisticated fraudulent activities, there is a pressing need for innovative solutions to safeguard customer assets and maintain trust in the financial system. The paper reviews existing literature, case studies, and methodologies to elucidate the transformative potential of RPA and AI in enhancing fraud detection capabilities within the banking sector. RPA streamlines operations, automates manual tasks, and accelerates data processing, while AI-driven predictive analytics analyze vast volumes of transaction data to identify patterns indicative of fraud. By combining the efficiency of automation with the intelligence of advanced analytics, banks can proactively detect and prevent fraudulent activities in real-time. The study also examines the challenges and opportunities associated with the deployment of RPA and AI-driven predictive analytics in banking, including data quality, regulatory compliance, model interpretability, and cybersecurity risks. Ethical considerations regarding data privacy, confidentiality, and responsible AI use are also discussed. Ultimately, this paper aims to provide insights into how banks can leverage RPA and AI technologies to strengthen their defenses against fraud, protect customer assets, and uphold the integrity of the financial system in the digital age. DOI: https://doi.org/10.52783/tjjpt.v43.i4.5531