IoT-Driven Visualization Framework for Enhancing Business Intelligence, Data Quality, and Risk Management in Corporate Financial Analytics

Abstract

Background Information: IoT proliferation has transformed the entire scenario of data-driven decision-making, especially in terms of corporate financial analytics. Traditional models often fail or have difficulty aligning with real-time data integration and anomaly detection. The study develops an IoT-driven visualization framework for business intelligence, data quality, and risk management to address the current limitations of such systems while integrating complex financial datasets. Objectives: The main idea is to propose a framework that integrates IoT data for real-time decision- making, predictive modeling, and anomaly detection in financial analytics. The framework improves processing speed, accuracy, and compliance adherence while providing actionable insights for stakeholders. This study assesses its performance through performance metrics and comparison with existing models. Methods: The proposed framework collects, preprocesses, and extracts features using IoT sensors. It uses predictive modeling and anomaly detection algorithms to analyze real-time financial data. Performance metrics such as accuracy, risk detection rate, and processing speed were measured. Ablation studies and comparisons with existing frameworks validated its efficacy. Results: The framework obtained 95% accuracy, 93% process speed, and a 95% risk detection rate. Ablation studies showed the importance of predictive modeling and anomaly detection. Comparison with others was done and proved better performance in financial analytics. Thus, the structure delivers the potential in terms of transforming decision-making approaches. Conclusion: The IoT-driven framework improves the quality of decisions made in corporate finance through high-quality data accuracy, risk detection, and regulation compliance. Future plans involve the introduction of blockchain and further expansion on IoT device capabilities. This work provides a platform for advanced analytics frameworks that serve practical solutions in dynamic financial settings