Crafting a Strong Anti-Fraud Defense: RPA, ML, and NLP Collaboration for resilience in US Finance’s

Anudeep Kotagiri, Abhinay Yada


This research paper explores the synergistic integration of Robotic Process Automation (RPA), Machine Learning (ML), and Natural Language Processing (NLP) in fortifying the anti-fraud defenses within the US finance sector. As financial systems become increasingly digitalized, the risk of fraudulent activities rises, necessitating advanced technological solutions. Our study delves into the collaborative potential of RPA, ML, and NLP to enhance resilience against evolving fraud tactics. We examine the effectiveness of automated processes in detecting anomalies, the adaptability of machine learning algorithms for real-time threat identification, and the linguistic analysis capabilities of NLP in uncovering subtle indicators of fraudulent behavior. By analyzing these technologies in tandem, this research contributes to the development of a robust and comprehensive anti-fraud framework, providing financial institutions with a sophisticated defense mechanism against emerging threats in the ever-evolving landscape of financial technology.

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