Revitalizing the Forensic Accounting: An Exploratory Study on Mitigating the Financial Risk using Data Analytics

Abstract

Risk mitigation and fraud prevention in the present times has more focus on digital metadata, wherein forensic accountants are required to make use of robust IT techniques and tools. Data Analytics has got huge implications for forensic accounting. With the ever-increasing electronic element of frauds in the present age of digitalization, and complexity of financial transactions and instruments, the challenges are growing multifold for the forensic accounting profession. The use of data analytics techniques, which enables the processing of large data in almost no time, simplify the task of forensic accountant to a large extent. It enables the identification of patterns and anomalies with the use of machine learning, deep learning, natural language processing, data mining and similar other statistical techniques. Beneish M-score and the Altman Z-score models are proven effective forensic accounting tools in fraud detection and insolvency prediction. This study has been undertaken to test the effectiveness and efficiency of these forensic tools in fraud detection and insolvency prediction with the case study of Bhushan Steel Ltd. Based upon the findings of the study, it is suggested that these models should be integrated with the data analytics, as these are highly effective and efficient in detection of financial statements frauds and this integration would equip the naïve investor in selection of financially sound company(s) to invest in. Data analytics is thus indispensable in the field of forensic accounting in the present era. Since, there is increased funding by many international funding agencies including the World Bank for empowering the forensic accountants and increasing awareness towards this profession, especially in developing countries; it can be hoped that the integration of data analytics with forensic accounting would gain momentum soon.