Big data analytics for financial auditing practices; Identification of conceptual patterns, implications and challenges using text mining

Resumen

Big data analytics and the practice of related technologies is rampant in the corporate world across the globe. The ability of companies to collect, store and analyze massive amounts of data and use such data for decisions is considered critical for a firm’s success. The auditing industry is not up to the mark, lacks sufficient emphasis and practice of big data analytics. This study assumes that big data processing technologies can impact financial auditing practices positively. Data sets were mined from literature using text mining methodology. Conceptual patterns such as Auditing, Fraud, Risk, and Security were found to be highly influential in the literature. Opinion in the literature is diverge for conceptual patterns such as Auditing, Fraud, and Risk but not for Security. Few potential implications under four main categories such as technologies, enablers, challenges, and compliance were identified. Digital technologies, specifically Artificial Intelligence (AI) and Blockchains were found to be enablers for firm’s performance and growth. Fraud detection, forensics, legitimacy were found to be few challenges for compliance. In addition to this, big data analytics (BDA) was found to be a moderating variable for technologies like Blockchains, Artificial Intelligence (AI) and for challenges such as Risk, Security but not for Fraud.