ROLE OF BIG DATA ANALYTICS IN FINANCIAL FRAUD DETECTION-A BIBLIOMETRIC ANALYSIS

Abstract

Using data analytics and machine learning to combat fraud is a strategy that many businesses have already considered. Fraud may be detected, investigated, and prevented with the aid of big data analytics and machine learning. The purpose of this research is to systematically review the 219 Scopus-indexed publications in context of data analytics in detecting financial crime during 1999 to 2022. The findings indicate that a significant portion of the literature focuses on the utilization of big data analytics, specifically machine learning and deep learning techniques, for the purpose of detecting credit fraud or financial statement fraud. Previo us studies have primarily concentrated on the utilization of hybrid technology in the realm of financial fraud detection, thereby indicating its potential as a promising avenue for future research. This study highlights the prominent research gap existing for a predictive model that can issue a warning as soon as a vulnerability for fraudulent behavior is noted. Moreover, findings highlight the accentuated need for data-driven financial investment model and stock market anomalies in context of data analytics and text mining, along with key future research agenda.