A Novel Framework for Text-Based Fraud Detection in Banking Using Spark NLP and Tableau

Abstract

Fraud detection in banking is evolving beyond numerical analysis to include unstructured text data, such as transaction notes and customer communications. This paper proposes a novel framework integrating Spark NLP for natural language processing, Great Expectations for data quality, AWS for scalable infrastructure, and Tableau for interactive visualizations to detect fraudulent patterns in text. Emphasizing a contextual fraud scoring approach, the framework combines entity recognition and sentiment analysis to identify suspicious activities with high precision. Designed for compliance with GDPR and PCI-DSS, it offers a scalable, modular solution adaptable to various banking applications. Using example datasets, we illustrate its potential to transform fraud detection. Mermaid diagrams and accessible language make this framework approachable for researchers, practitioners, and non-experts alike.