Project Abstract

Abstract
The banking sector is no stranger to fraudulent activities, which pose a significant threat to financial institutions and their customers. Traditional methods of fraud detection are often reactive and fall short in identifying sophisticated fraudulent schemes in a timely manner. This research explores the application of artificial intelligence (AI) in enhancing fraud detection capabilities within banking systems. The study aims to investigate the effectiveness of AI technologies, such as machine learning algorithms, neural networks, and natural language processing, in detecting and preventing fraudulent activities in real-time. The research begins with an in-depth examination of the background of the study, highlighting the prevalence of fraud in the banking sector and the limitations of existing fraud detection methods. The problem statement underscores the need for more advanced and proactive approaches to combatting fraud in banking systems. The objectives of the study are outlined to guide the research process towards achieving specific goals, including improving fraud detection accuracy and reducing false positives. The literature review delves into existing research on AI applications in fraud detection, exploring various algorithms and techniques employed in this field. The chapter synthesizes findings from previous studies to identify best practices and potential gaps in the current body of knowledge. The research methodology section outlines the approach taken to conduct the study, including data collection methods, sample selection, and the implementation of AI models for fraud detection. Chapter four presents a detailed discussion of the research findings, focusing on the performance of AI algorithms in detecting fraudulent activities within banking systems. The chapter analyzes the effectiveness of different AI models, their strengths, limitations, and implications for real-world applications. The discussion also examines factors influencing the success of AI-based fraud detection systems, such as data quality, model interpretability, and scalability. Finally, the conclusion summarizes the key findings of the research and offers insights into the potential benefits of integrating AI technologies into banking systems for fraud detection purposes. The study underscores the significance of leveraging AI to enhance fraud detection capabilities, improve operational efficiency, and protect the financial interests of banks and their customers. The research contributes to the growing body of knowledge on AI applications in the banking sector and provides valuable insights for practitioners, researchers, and policymakers seeking to combat financial fraud effectively.

Project Overview