1 Master of Computer Science, Dept. of Computer Science, Maharishi International University, Iowa USA.
2 Master of Science in Accounting, Kogod School of Business, American University, Washington DC USA.
3 Master of Business Administration, Dept. of Business Management Maharishi International University, Iowa USA.
Research Article
World Journal of Advanced Research and Reviews, 2024, 23(02), 697–707
Received on 26 June 2024; revised on 06 August 2024; accepted on 08 August 2024
This research investigates the application of advanced technologies, specifically machine learning (ML), natural language processing (NLP), and generative artificial intelligence (AI), to enhance regulatory compliance and fraud detection within the financial services sector. Machine learning, with its ability to analyze vast amounts of data and identify patterns, provides predictive capabilities that can significantly improve the accuracy of fraud detection systems. NLP, on the other hand, offers a nuanced understanding of textual data, facilitating more efficient processing of compliance documentation and communication logs. Generative AI introduces innovative approaches by simulating potential fraud scenarios, thereby allowing organizations to anticipate and mitigate emerging threats.
The study aims to integrate these technologies into a cohesive framework that enhances both the detection of fraudulent activities and the efficiency of compliance processes. By leveraging ML's predictive power, NLP's textual analysis capabilities, and generative AI's scenario simulation, this research seeks to address existing limitations in traditional fraud detection and regulatory adherence systems. Traditional methods often struggle with adapting to new fraud tactics and managing large volumes of compliance data, leading to inefficiencies and increased vulnerability.
Key findings of this research demonstrate that the implementation of machine learning algorithms results in a 30% increase in fraud detection accuracy and a 25% reduction in false positives compared to conventional approaches. NLP techniques have been shown to enhance processing efficiency for compliance documentation by 40%, reducing errors and speeding up the review process. Additionally, generative AI models have contributed to a 35% improvement in predicting and addressing potential fraud scenarios, thus enhancing overall system robustness.
This study provides a comprehensive examination of methodologies, benefits, and future directions for deploying ML, NLP, and generative AI in financial services. It underscores the transformative potential of these technologies in strengthening security measures, ensuring meticulous adherence to evolving regulatory standards, and fostering a trustworthy operational environment. The integration of these advanced technologies promises not only to bolster the security framework but also to offer a more dynamic and adaptive approach to regulatory compliance and fraud detection.
Fraud Detection; Machine Learning; NLP; Generative AI; Regulatory Compliance; Cybersecurity
Preview Article PDF
Iga Daniel Ssetimba, Jimmy Kato, Eria Othieno Pinyi, Evans Twineamatsiko, Harriet Norah Nakayenga and Eudis Muhangi. Advancing electronic communication Compliance and fraud detection Through Machine Learning, NLP and generative AI: A Pathway to Enhanced Cybersecurity and Regulatory Adherence. World Journal of Advanced Research and Reviews, 2024, 23(2), 697-707. Article DOI: https://doi.org/10.30574/wjarr.2024.23.2.2364