Advances In Predictive Modeling and Risk Mitigation in Education and Financial Services using Machine Learning and BI Dashboards

This paper examines how predictive modeling and risk mitigation techniques based on machine learning and Business Intelligence (BI) dashboards are being integrated into education and financial services. It discusses how ML-driven predictive models support student performance monitoring, dropout prediction, and risk assessment in education, enabling proactive interventions. In financial services, the paper highlights applications in credit risk evaluation, fraud detection, and regulatory compliance to improve decision-making. It also identifies major challenges, including data quality issues, algorithmic bias, lack of transparency, and high implementation costs that hinder large-scale adoption. The authors emphasize the need for better data integration, fairness in model outcomes, and effective use of BI dashboards to support decisions. Future directions include incorporating more advanced AI techniques and improving BI tools to enhance predictive accuracy and risk mitigation. Overall, the paper underscores the transformative potential of predictive analytics for improving outcomes, operational efficiency, and risk management across both sectors.