Optimization of Artificial Intelligence in Internal Audit to Enhance the Effectiveness of Financial Fraud Detection Based on Real-Time Data

Abstract

Introduction: In an increasingly complex digital era, financial fraud is a serious threat to organizations around the world. Conventional audit methods are often unable to identify patterns of fraud hidden in huge transaction data. Therefore, the application of Artificial Intelligence (AI) in internal audits is a promising solution to increase the effectiveness of fraud detection based on real-time data.

Objectives: This study aims to identify the key factors that affect the successful implementation of AI in internal audit, develop an optimization model to improve the accuracy and speed of financial fraud detection, and formulate strategic recommendations for organizations in adopting AI as a financial supervision tool.

Methods: This study uses a qualitative approach with a literature study method on various recent studies related to the application of AI in financial auditing.

Results: The results show that the success of AI implementation depends on data quality, the right algorithm selection, the readiness of the technology infrastructure, as well as management support and regulatory compliance. Effective optimization models include the implementation of hybrid AI, real-time-based predictive systems, periodic model calibration, and AI integration with existing internal audit systems.

Conclusions: By adopting a holistic AI optimization strategy, organizations can improve accuracy and efficiency in detecting financial fraud, thereby strengthening their financial transparency and governance.