Thesis Abstract

Abstract
The banking industry plays a crucial role in the economy, providing financial services to individuals and businesses while also facing the challenge of fraud. With the advancement of technology, artificial intelligence has emerged as a powerful tool in enhancing fraud detection and prevention strategies within banking operations. This thesis explores the application of artificial intelligence in fraud detection for banking operations, focusing on its effectiveness in mitigating financial risks and safeguarding the integrity of banking systems. Chapter One provides an introduction to the study, presenting the background of the research, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. Chapter Two delves into a comprehensive literature review, examining existing studies, theories, and methodologies related to artificial intelligence in fraud detection and banking operations. The literature review identifies key trends, challenges, and best practices in this field, providing a foundational framework for the subsequent chapters. Chapter Three outlines the research methodology employed in this study, including research design, data collection methods, data analysis techniques, and ethical considerations. The chapter elaborates on the process of data collection, model development, and evaluation criteria for assessing the effectiveness of artificial intelligence in fraud detection within banking operations. Chapter Four presents a detailed discussion of the findings obtained from the research, highlighting the impact of artificial intelligence on fraud detection accuracy, speed, and cost-effectiveness in banking operations. The chapter explores the practical implications of implementing artificial intelligence solutions, such as machine learning algorithms and predictive analytics, to detect and prevent fraudulent activities within banking systems. Chapter Five concludes the thesis by summarizing the key findings, implications, and recommendations for future research and industry practices. The study underscores the importance of leveraging artificial intelligence technologies to enhance fraud detection capabilities in banking operations, ultimately improving financial security, customer trust, and regulatory compliance within the banking sector. In conclusion, this thesis contributes to the growing body of knowledge on the application of artificial intelligence in fraud detection for banking operations. By harnessing the power of advanced technologies, banks can proactively identify and prevent fraudulent activities, safeguarding their assets and reputation in an increasingly complex and interconnected financial landscape.

Thesis Overview