Artificial Intelligence Driven Approaches for Financial Fraud Detection: A Systematic Literature Review

Abstract

The primary aim of this research is to present a thorough and all-encompassing examination of artificial intelligence (AI) methodologies employed in the detection of financial fraud. The present study employs a systematic literature review (SLR) that was conducted utilizing the PRISMA approach. A comprehensive search was undertaken on reputable academic databases including ScienceDirect, Scopus, Springer, and Emerald, yielding a total of 24 papers published throughout the timeframe of 2014 to 2023. These articles will, thereafter, undergo further analysis. The findings of this study demonstrate that the implementation of artificial intelligence (AI) techniques for detecting financial fraud yields favorable outcomes. Specifically, the AI approach proves to be effective in enhancing the precision and efficiency of fraud pattern identification, thereby making a substantial contribution in this domain. In contrast, the prevailing methodology employed in the realm of financial fraud detection is frequently centered around machine learning. Furthermore, a majority of the research encompassed a diverse range of industries, with particular emphasis on the financial industry as the primary domain for the implementation of artificial intelligence (AI) in the detection of financial fraud. Keywords: artificial intelligent, financial fraud, fraud detection