Review Analysis: Digital Detection to Identofy Fraud Exposures in Company Financial Statements
Abstract
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.
References
Abdallah, A., Maarof, M. A., & Zainal, A. (2016). Fraud detection system: A survey. Journal of Network and Computer Applications, 68, 90-113.
AK, U. N. S. (2023). Pengaruh Internal Control, Good Public Governance, Budaya Organisasi, Tekanan Target, Kompensasi Dan Arogansi Terhadap Pencegahan Fraud Pada Penggunaan Dana Desa.
Anthony, C. A. F., Gaol, W. N. A. L., Purba, H. N. N., Raudina, H. C., & Maulana, A. (2023). Peranan Audit Internal dalam Pengendalian Fraud di Era Digital. Accounting Student Research Journal, 2(1), 31-45.
Apriyeni, Y., & Aprila, N. (2024). Pengaruh Dimensi Fraud Crowe Pentagon terhadap Kecurangan Akademik Mahasiswa. Reslaj: Religion Education Social Laa Roiba Journal, 6(4), 1803-1817.
Bhavani, G., & Amponsah, C. T. (2017). M-Score and Z-Score for detection of accounting fraud. Accountancy Business and the Public Interest, 1(1), 68-86.
Chatterjee, P., Das, D., & Rawat, D. B. (2024). Digital twin for credit card fraud detection: Opportunities, challenges, and fraud detection advancements. Future Generation Computer Systems.
Hogan, C. E., Rezaee, Z., Riley Jr, R. A., & Velury, U. K. (2008). Financial statement fraud: Insights from the academic literature. Auditing: A Journal of Practice & Theory, 27(2), 231-252.
Kaur, B., Sood, K., & Grima, S. (2023). A systematic review on forensic accounting and its contribution towards fraud detection and prevention. Journal of Financial Regulation and Compliance, 31(1), 60-95.
Kustiwi, Irda Agustin. 2024. “Accounting Information System (Ais): Integration of Artificial Intelligence and Management in Farm Tourism Kelompok Tani Elok Mekar Sari.” DiE: Jurnal Ilmu Ekonomi Dan Manajemen 15(1):123–31. doi: 10.30996/die.v15i1.10634.
Mandal, A. (2023). Preventing financial statement fraud in the corporate sector: insights from auditors. Journal of Financial Reporting and Accounting.
Manuputty, G. P., Azis, A. A., & Pratami, N. A. N. (2022). Analisis Manajemen Risiko Berbasis ISO 31000 Pada Aspek Operasional Teknologi Informasi PT. Schlumberger Geophysics Nusantara. E-Prosiding Akuntansi, 3(1).
Nurcholisah, K., Purnamasari, P., & Sukarmanto, E. (2023). Analisis Tren Kecurangan Laporan Keuangan Perbankan: Pre Dan Post Pandemic Covid Di Indonesia. Jurnal Reviu Akuntansi dan Keuangan, 13(1), 202-215.
OKENWA, O. C. (2021). EFFECTIVE DEPLOY OF DIGITAL FORENSIC TECHNIQUES AND THE SUSTENANCE OF MATERIAL MISSTATEMENT-FREE FINANCIAL REPORTING IN NIGERIA. Journal of Academic Research in Economics, 13(3).
Prasetiyo, Y. P., Paramitha, D., Riyani, E. I., & Mubarok, F. (2023). Integrasi Penerapan Akuntansi Forensik dan Audit Investigatif dalam Mendeteksi Fraud: Studi Literatur. Jurnal Buana Akuntansi, 8(1), 16-29.
Ratnawati,T. Kustiwi, I. A., etc. 2023. Internal Audit of Cupak Village Fund Budgetting Process, Nguskan, Jombang. International Journal Of Social Science Humanity & Management Research, Volume 02 Issue 09 September 2023, Page No. 987-999. DOI: 10.58806/ijsshmr.2023.v2i9n17
ROZA, M., & MUHAMMAD, N. (2020). Pengaruh Audit Forensik, Audit Investigasi, Profesionalisme terhadap Pencegahan Kecurangan (Studi Empiris pada BPKP Provinsi Banten). Jurnal Riset Akuntansi Terpadu, 13(2), 272-294.
Safitri, S., Rahayu, P., & Aguspriyani, Y. (2024). Peran Internal Audit Dalam Mendeteksi Kecurangan pada Perbankan Syariah di Indonesia. IJM: Indonesian Journal of Multidisciplinary, 2(1), 74-83.
Sawangarreerak, S., & Thanathamathee, P. (2021). Detecting and analyzing fraudulent patterns of financial statement for open innovation using discretization and association rule mining. Journal of Open Innovation: Technology, Market, and Complexity, 7(2), 128.
Simbolon, A. Y., & Kuntadi, C. (2022). Pengaruh Penerapan Akuntansi Forensik, Whistle Blowing System, Dan Budaya Organisasi Terhadap Pencegahan Fraud (Literature Review Akuntasi Forensik). Jurnal Economina, 1(4), 849-860.
Sipayung, Y. A. (2024). Pengaruh Dimensi Fraud Crowe Pentagon terhadap Kecurangan Akademik Mahasiswa. Reslaj: Religion Education Social Laa Roiba Journal, 6(3), 1723-1736.
Zhang, W., Chen, R. S., Chen, Y. C., Lu, S. Y., Xiong, N., & Chen, C. M. (2019). An effective digital system for intelligent financial environments. IEEE Access, 7, 155965-155976.
Zhu, X., Ao, X., Qin, Z., Chang, Y., Liu, Y., He, Q., & Li, J. (2021). Intelligent financial fraud detection practices in post-pandemic era. The Innovation, 2(4).
