Analysis of the Impact of Artificial Intelligence on Financial Reporting in Small and Medium Enterprises

Thesis Abstract

Abstract
This thesis investigates the impact of artificial intelligence (AI) on financial reporting within small and medium enterprises (SMEs). In recent years, AI has emerged as a transformative technology with the potential to revolutionize various industries, including accounting and finance. However, the specific implications of AI adoption on financial reporting practices in SMEs have not been extensively explored. This research aims to fill this gap by analyzing how AI technologies influence financial reporting processes, accuracy, efficiency, and decision-making within SMEs. The study begins with an introduction outlining the significance of the research topic and providing an overview of the objectives, scope, and structure of the thesis. The background of the study highlights the growing importance of AI in the accounting field and the specific relevance of this research to SMEs. The problem statement emphasizes the need to understand the implications of AI on financial reporting in SMEs to enhance their competitiveness and sustainability in the digital era. The objectives of the study are to assess the current landscape of financial reporting in SMEs, evaluate the potential benefits and challenges of AI adoption, and provide recommendations for successful integration. The limitations of the study are acknowledged, including the focus on a specific sector of SMEs and the availability of relevant data and resources. The scope of the study is defined to encompass the analysis of AI technologies such as machine learning, natural language processing, and robotic process automation in financial reporting contexts. A comprehensive literature review is conducted in Chapter Two, which explores existing research on AI applications in accounting, financial reporting, and SMEs. The review identifies key themes related to AI adoption challenges, benefits, and best practices in financial reporting processes. The research methodology in Chapter Three outlines the quantitative and qualitative approaches used to collect and analyze data from SMEs regarding their AI adoption experiences and financial reporting practices. Chapter Four presents the findings of the study, revealing the specific ways in which AI technologies impact financial reporting accuracy, efficiency, and decision-making in SMEs. The discussion delves into the implications of these findings for SMEs seeking to leverage AI for competitive advantage in the evolving business landscape. Finally, Chapter Five provides a summary of the research findings, conclusions drawn from the analysis, and recommendations for future research and practical implications for SMEs. Overall, this thesis contributes to the growing body of knowledge on the intersection of AI and financial reporting in SMEs, offering insights into the opportunities and challenges presented by AI technologies. The research findings have implications for SMEs looking to enhance their financial reporting practices through AI adoption and can inform policymakers, practitioners, and academics in the accounting and finance fields.