This paper explores the integration of Information Technology (IT), specifically Machine Learning (ML) and Data Analytics, in combating banking fraud. Traditional fraud detection methods, relying heavily on rule-based systems, have proven inadequate against sophisticated fraud techniques. The paper highlights the effectiveness of various machine learning models and the role of big data analytics in enhancing detection accuracy and real-time processing. It discusses the challenges and limitations of implementing these technologies, including data quality, model interpretability, integration with existing systems, and ethical and regulatory considerations. The study also provides a comparative analysis of data analytics tools such as SAS AML, Shell Scripting, and Data Integration Studio, and offers practical insights for financial institutions seeking to strengthen their defenses against fraud. The findings emphasize the transformative potential of IT-driven approaches in revolutionizing fraud detection and prevention.This paper explores the integration of Information Technology (IT), specifically Machine Learning (ML) and Data Analytics, in combating banking fraud. Traditional fraud detection methods, relying heavily on rule-based systems, have proven inadequate against sophisticated fraud techniques. The paper highlights the effectiveness of various machine learning models and the role of big data analytics in enhancing detection accuracy and real-time processing. It discusses the challenges and limitations of implementing these technologies, including data quality, model interpretability, integration with existing systems, and ethical and regulatory considerations. The study also provides a comparative analysis of data analytics tools such as SAS AML, Shell Scripting, and Data Integration Studio, and offers practical insights for financial institutions seeking to strengthen their defenses against fraud. The findings emphasize the transformative potential of IT-driven approaches in revolutionizing fraud detection and prevention.