Raja Rehan

Internal Financial Control (IFC) is a critical component of corporate governance, ensuring the accuracy, reliability, and compliance of financial reporting. Traditional IFC systems rely on manual audits, centralized databases, and rule-based checks, which are often inefficient, prone to human error, and vulnerable to fraud. The integration of Blockchain Technology and Machine Learning (ML) has introduced transformative improvements in Internal Financial Control (IFC) systems. This paper explores how Blockchain and machine learning (ML) technologies can strengthen internal financial controls (IFC). By addressing limitations in traditional systems, these technologies introduce transparency, automation, and predictive capability, fostering enhanced compliance and reduced risk. The integration of these technologies offers a paradigm shift for governance, risk management, and auditing practices, enhances fraud detection and regulatory compliance, while addressing challenges such as scalability and data privacy. Through a synthesis of academic literature and industry case studies, Blockchain ensures immutable transaction records, while ML enables predictive anomaly detection. Blockchain and ML are transforming internal financial control by enhancing security, automation, and predictive capabilities. There are still challenges in overcoming scalability, interpretability, Hybrid Blockchain-ML frameworks, and regulatory challenges for widespread adoption.