A Systematic Review for Fraud Detection in Financial Transactions using Machine Learning

Abstract

The increasing number of digital platforms in the modern financial environment has greatly increased the complexity and magnitude of fraudulent activity. This systematic study thoroughly examines the utilization of machine learning (ML) in identifying fraudulent activities in financial transactions. It also analyzes the complexities of the algorithms involved. Moreover, the paper clarifies the crucial significance of assessment metrics in measuring the effectiveness and adaptability of models across various datasets and fraud situations. To summarize, this systematic review offers a thorough analysis of the field of fraud detection in financial transactions through the utilization of machine learning.