Review Analysis: Digital Detection to Identofy Fraud Exposures in Company Financial Statements - Rosyida, Isnaini Anniswati (International Conference On Economics Business Management And Accounting (ICOEMA), 2024)
Digital detection to identify fraud exposures in corporate financial reports has become an important focus in efforts to improve the accuracy, efficiency and transparency of financial audits. In this review analysis, we explore various technologies and methodologies used in fraud detection, including artificial intelligence (AI), machine learning (ML), and big data analytics. We highlight the key benefits of a digital approach, including the ability to identify suspicious patterns with high accuracy, improve the efficiency of the audit process, and strengthen the integrity of financial reports. However, we also identified a number of challenges faced in implementing digital detection, including issues of data integrity and quality, privacy and security, as well as the need for specialized skills and significant investment. In this context, we advise companies to invest in technological infrastructure and human resource training, as well as ensure compliance with applicable data privacy regulations. We also emphasize the importance of adopting a more open culture towards technological innovation and working with regulators to develop frameworks that support the ethical and effective use of digital technologies. By addressing these challenges, companies can harness the full potential of digital detection to increase trust and accountability among stakeholders, and drive positive change in business practices and financial regulations.