| Abstract |
This study conducts a comprehensive literature review to explore and analyze the
diverse array of machine learning methods employed in the realm of financial fraud detection. The
primary objective is to provide a thorough understanding of the current state-of-the-art, identifying
key methodologies, trends, and challenges in this critical domain. The literature review encompasses
a wide range of scholarly articles, journals, and conference proceedings, systematically examining
the application of various machine learning techniques in financial fraud detection. Supervised
learning methods, such as logistic regression and support vector machines, and unsupervised
techniques, including clustering algorithms like k-means, are explored in the context of their
effectiveness in identifying fraudulent activities. Key findings highlight the evolution of machine
learning models specifically tailored for addressing the dynamic nature of financial fraud. The
study emphasizes the importance of feature engineering, data preprocessing, and model
interpretability in enhancing the overall efficacy of machine learning-based fraud detection systems.
This research study identifies emerging trends, such as the integration of artificial intelligence
(AI) and advanced analytics, contributing to the development of more sophisticated and adaptive
fraud detection mechanisms. The study acknowledges notable achievements but also addresses
persistent challenges, including the demand for large and diverse datasets and the need for model
explain ability in the context of financial fraud detection. |
