Internal Control and Risk Management in Accounting Information System

Abstract

In the service sector, accounting information systems face growing risks in data security, unauthorized access, and fraud. Strong internal control and risk management are essential for efficiency and trust. This study proposes an intelligent accounting information system with four modules: (1) data collection for financial and operational data completeness; (2) information encryption using Advanced Encryption Standard with 256-bit key in Galois/Counter Mode (AES-256-GCM), Rivest-Shamir-Adleman (RSA), and Elliptic Curve Digital Signature Algorithm (ECDSA); (3) real-time risk assessment based on probability and impact; and (4) decision support comparing linear regression, support vector machines, and artificial neural networks for cost prediction. Results show the artificial neural network achieves the highest accuracy and is adopted for cost optimization and budgeting. The system enhances security, enables proactive risk management, and supports data-driven decisions.