Utilizing business analytics to combat financial fraud and enhance economic integrity

Financial fraud poses a significant threat to economic stability, with traditional detection methods often struggling to keep pace with increasingly sophisticated schemes. This paper explores the role of business analytics in enhancing fraud detection and maintaining economic integrity. Utilizing advanced techniques such as machine learning, anomaly detection, and clustering, business analytics offers a proactive approach to identifying fraudulent patterns and mitigating financial risks. The research discusses the development of a comprehensive fraud detection model that emphasizes transparency, accountability, and regulatory compliance, fostering a more secure financial environment. Through a comparison with conventional fraud detection methods, this study highlights the superior efficiency, accuracy, and adaptability of analytics-driven approaches. Implications for financial institutions and policymakers are addressed, emphasizing the need for supportive regulations and privacy considerations. Finally, the study outlines future research directions, including the integration of artificial intelligence and blockchain technology in fraud prevention systems. The findings demonstrate that business analytics plays a critical role in fortifying economic integrity by advancing fraud detection capabilities.