AI-Powered Fraud Detection in BI Systems Using Machine Learning: A Behavioral Biometric Approach

Abstract

This paper has gone further and identified how BI system had changed and handle the fraud detection after adding the concept of AI. What kind of significant transformation has undergone when business intelligence integrated with AI? How a BI enhance the decision-making process competitiveness and productivity in the modern context including the AI within contemporary business conditions. When we talk about the updated concept of BI, it added different AI technology is such as machine learning predictive analysis natural language processing (NLP) with BI. It also discussed how BI monitor the historical data and defined queries to measure the performance, what challenges BI faced in scalability, speed and adaptability. This paper explores that, in today’s era where data collection and analysis are the part of business environment, how AI particularly ML enables the fraud detection capability into BI system. How a BI system can analysed large data set in real time, unhide the hidden pattern and detect anomalies to identify the fraudulent behaviour of customer, employees (insider threats) or cyber criminals. It also enables the predictive analysis for forecasting the future events and perspective analytics for optimal action. They just not only identify the fraud but also anticipate the potential risk and take proactive measures. This research highlights the architecture, methodologies, and benefits of implementing AI for fraud detection in BI system, demonstrating that how an integration leads to more intelligent, responsive, and secure business operations. A major focus of the research is the application of AI, especially ML, in fraud detection. By analyzing large datasets in real time, AI-integrated BI systems can detect suspicious behaviors be it from customers, internal employees (insider threats), or cybercriminals. These systems go beyond identifying existing fraud by anticipating potential risks and enabling proactive responses.