Internal Controls

This study aimed to develop an internal control model for the Iranian Social Security Organization with a risk management approach. This exploratory study is applied in terms of objective, and the statistical population comprised 340 employees in the financial department of the Iranian Social Security Organization. The data was collected using interviews and questionnaires, and the hypotheses were tested with inferential statistics and structural equation tests. The results showed that the internal control factors of the Iranian Social Security Organization include the income bank system, the legal obligations system, compensation, the financial bookkeeping system, the movable and immovable property system, the financial management system, and check issuance requirements. The results also showed that the factors affecting internal controls are among the factors influencing risk management. Considering the extent of branches, dimensions, various services including (retirement, disability) and the size and volume of financial operations, as well as the very high budget of the Social Security Organization (for example, in 1401, the budget of the Social Security Organization is 500 thousand billion Rials), there are many factors on the controls Empty and effective risk management. The most important factors affecting the Social Security Organization's internal controls' efficacy are the financial bookkeeping system, Payroll system, And Legal requirements for issuing checks.

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.