A study on big data adoption in financial accounting and its implications for financial statement accuracy

Financial accounting has been changed by using vast datas to increase the accuracy of financial statements with big data. In this paper, big data in financial accounting adoption process, insights from data driven and real world cases are studied and its implication for accuracy of financial statements is also investigated. It investigates how these set of characteristics or paradigms of big data (volume, veracity, velocity and variety) facilitate real time transaction analysis and help to minimize the errors there are in periodic reporting which are prone to errors. The study also demonstrates how machine learning plays in big data detection fraud, through PayPals real time fraud monitoring, such as in the case of fraud detection, the use of machine learning to spot anomalies and to prevent financial misstatements. Furthermore, it studies Googles heavy use of search trends as a case study in enhancing financial forecasting through big data. Finally, the paper also covers auditing advances to auditing entire transaction populations and predictive analytics, as these are KPMGs auditing practices. However, visualization tools like Power BI and the yet to be understood technology of blockchain make accuracy and transparency even better, but challenges remain at first such as infrastructure limitations and skill gap. The findings imply that big data aims to transform financial accounting, yet it is constrained by these hurdles that hinder the full potential of the data revolution in terms of more reliable and transparent reporting of the financials.