Exploring the Impact of Predictive Analytics on Accounting and Auditing Expertise: A Regression Analysis of LinkedIn Survey Data

Considering the recent development of big data and its application in various business and management endeavors, there is a growing need for businesses and management of organizations to engage large amounts of data to make real-time decisions, improve financial reporting techniques, and optimize risk management systems in order to ensure increased effectiveness and efficiency in managing the financial resources of the organization. Also, to enhance auditing proficiency and detect fraudulent activities, auditing professionals are constantly engaged in routine tasks while conducting their professional engagements. This paper investigates the impacts of predictive analytics on accounting and auditing proficiency, focusing on financial reporting, fraud detection, risk management, and real-time decision-making. Predictive analytics is a data-driven approach that utilizes historical data and advanced modeling techniques to forecast future events and trends. Thus, this study aims to determine if integrating predictive analytics in accounting and auditing enhances heightened accuracy and reliability within these critical functions. The paper collected primary data through survey questionnaires from 366 accounting and auditing professionals with over ten years of experience. The data collected proved reliable as subjected to a Cronbach alpha reliability test. Linear regression was employed to test the hypothesis. The study found a positive significant relationship between predictive analytics and financial reporting accuracy, fraud detection, real-time decision-making, and risk management. The study recommends that organizations should embrace and invest in predictive analytics technologies in order to enhance their financial performance, while accounting and auditing professionals should commit to continuous learning and skills development in machine learning in order to heighten their proficiency in building proficient predictive analytics models that effectively assess risks, detect fraudulent actions, increase financial reporting accuracy, and supports real-time decision making.