Fraud Detection in Fintech Leveraging Machine Learning and Behavioral Analytics

Abstract

Fraud detection in the fintech sector is a critical area of concern as financial transactions increasingly shift to digital platforms. This paper presents a comprehensive analysis of enhancing fraud detection in fintech by combining machine learning techniques, leveraging behavioral analytics, and adopting RegTech solutions. The objective is to develop a holistic approach that strengthens fraud prevention strategies, ensures regulatory compliance, and safeguards the interests of customers and financial institutions. The paper begins with an introduction that sets the context by highlighting the growing importance of fraud detection in the digital financial landscape. It outlines the research objectives, scope, and structure of the paper. Subsequently, the methodology section details the data collection process, the selection and comparative analysis of machine learning models, the integration of behavioral analytics, and the implementation of RegTech solutions. The paper concludes with a summary of findings and contributions, emphasizing the significance of adopting a holistic approach to fraud detection in the fintech industry. It underscores the need for financial institutions to embrace advanced technologies, comply with data privacy regulations, and collaborate within the industry to combat financial crimes effectively.