Natural Language Processing for Fraud Detection in Financial Transactions: A Synergy of Mathematics and Computer Science

Author(s): Rashmi N, Roshini Anne Koshy, S Sushanth and Joswin Dsouza

Abstract:

The rise of digital financial transactions has led to an unprecedented surge in fraudulent activities, necessitating advanced methodologies for timely detection and prevention. This paper explores the integration of Natural Language Processing (NLP) techniques with mathematical models to enhance the efficacy of fraud detection in financial transactions. The convergence of mathematics and computer science plays a pivotal role in developing a robust framework capable of discerning intricate patterns indicative of fraudulent behaviour. The mathematical foundation of the proposed approach involves the utilisation of statistical models, machine learning algorithms, and anomaly detection techniques. By leveraging mathematical concepts such as probability theory, regression analysis, and clustering algorithms, the system aims to identify irregularities and deviations from established transaction patterns. This mathematical underpinning provides a solid framework for capturing subtle nuances that may be indicative of fraudulent activities. In parallel, the synergy with computer science manifests through the incorporation of NLP techniques, allowing the system to analyse and understand the textual components associated with financial transactions. Textual information, such as transaction descriptions, user comments, and contextual data, is processed using natural language understanding algorithms. This linguistic analysis contributes to a holistic understanding of transactional data, enabling the system to identify anomalies that may not be apparent through traditional numerical analysis alone. The proposed framework integrates mathematical models and NLP algorithms in a unified system, facilitating the development of a comprehensive fraud detection system for financial transactions. Real-world data experiments demonstrate the system's ability to detect previously undetected fraud patterns, showcasing the effectiveness of the combined approach. As the financial landscape continues to evolve, the fusion of mathematics and computer science in the realm of fraud detection presents a promising avenue for strengthening security measures in the digital financial domain.

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Pages: 46-48     |    2 View     |    0 Download