Thesis Abstract

Abstract
The integration of artificial intelligence (AI) technologies into various industries has revolutionized business processes and decision-making. In the field of accounting, AI has the potential to significantly impact financial statement analysis by enhancing accuracy, efficiency, and predictive capabilities. This thesis aims to explore the implications of integrating AI into financial statement analysis practices in accounting. The research begins with a comprehensive introduction that delves into the background of the study, outlining the evolution of AI technologies and their increasing relevance in accounting practices. The problem statement identifies the current challenges and limitations faced in traditional financial statement analysis methods and highlights the need for AI integration. The objectives of the study are to evaluate the benefits and challenges of incorporating AI in financial statement analysis, assess the impact on decision-making processes, and provide recommendations for effective implementation strategies. The study acknowledges the limitations of the research, including data availability constraints and potential biases in AI algorithms. The scope of the study focuses on exploring the impact of AI specifically on financial statement analysis within the accounting domain. The significance of the research lies in its contribution to the growing body of knowledge on AI applications in accounting and its potential to inform future practices and policies. The structure of the thesis is outlined, detailing the chapters and sub-sections that will guide the reader through the research journey. Definitions of key terms are provided to establish a common understanding of AI and financial statement analysis concepts. The literature review in Chapter Two critically analyzes relevant studies and publications on AI applications in financial statement analysis. Ten key themes are identified, including AI algorithms, machine learning techniques, data analytics, and decision support systems. Each theme is discussed in detail to provide a comprehensive overview of the current state of research in the field. Chapter Three presents the research methodology, detailing the research design, data collection methods, sampling techniques, and data analysis procedures. Eight contents are discussed, including the selection of AI tools, data sources, model validation techniques, and ethical considerations in AI implementation. Chapter Four presents the findings of the study, analyzing the impact of AI on financial statement analysis based on empirical data and case studies. The discussion explores the effectiveness of AI algorithms in predicting financial trends, identifying anomalies, and enhancing decision-making processes in accounting practices. Finally, Chapter Five concludes the thesis by summarizing the key findings, implications, and recommendations for future research and practice. The conclusion highlights the transformative potential of AI in financial statement analysis and emphasizes the need for continuous evaluation and improvement to ensure ethical and reliable AI integration in accounting practices. Overall, this thesis contributes to the understanding of the impact of AI on financial statement analysis in accounting, providing insights that can inform decision-makers, researchers, and practitioners in leveraging AI technologies for enhanced financial analysis and reporting.

Thesis Overview