Application of artificial intelligence in the prevention of fraud in financial statements

Authors

  • Rina Septiriana Universitas Tanjungpura
  • Septian Rheno Widianto Universitas Bina Sarana Informatika
  • Putri Ekawati Darma Fakultas Ekonomi dan Bisnis, Universitas Khairun

Keywords:

Artificial intelligence (AI), ACL Analytic, Financial statement fraud prevention, Data analysis, Case study

Abstract

This study examines the use of artificial intelligence (AI) through the ACL Analytic programme to avoid fraud on financial accounts. The findings demonstrate that the ACL Analytic programme is proficient in detecting possibly deceptive transactions, enhancing temporal efficiency in the audit procedure, and aiding in scrutinising substantial amounts of data with superior swiftness and precision compared to conventional approaches. Traditional techniques for preventing fraud are often inadequate against the evolving strategies employed by fraudsters, necessitating a more advanced and adaptable strategy. Due to the exponential increase in data volume, firms have challenges in conducting comprehensive analysis of financial information. Artificial intelligence (AI) presents a potential solution to this problem by offering the capability to process and analyse data on a significantly greater magnitude. The research employed a case study approach, enabling the researcher to thoroughly examine the implementation of artificial intelligence using the ACL Analytic application within the realm of preventing financial statement fraud. The study's findings offer a thorough comprehension of the efficacy of utilising artificial intelligence via ACL Analytic for the purpose of preventing financial statement fraud. Additionally, it offers valuable insights for other companies and financial institutions considering the adoption of similar technology.

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References

D. Sasmita and Kimsen, “Jurnal Ekonomi Akuntansi,” Fak. Ekon. Univ. Muhammadiyah Tangerang, vol. V, no. Persediaan, pp. 1–12, 2014.

R. S. Y. Zebua et al., FENOMENA ARTIFICIAL INTELLIGENCE (AI). PT. Sonpedia Publishing Indonesia, 2023.

M. S. Maulana, S. R. Widianto, S. D. A. Safitri, and R. Maulana, “Pelatihan Chat GPT sebagai Alat Pembelajaran Berbasis Artificial Intelligence di Kelas,” J. Penelit. dan Pengabdi. Masy. Jotika, vol. 3, no. 1, pp. 16–19, 2023.

M. A. Aditya, R. D. Mulyana, I. P. Eka, and S. R. Widianto, “Penggabungan Teknologi Untuk Analisa Data Berbasis Data Science,” in Seminar Nasional Teknologi Komputer & Sains (SAINTEKS), 2020, vol. 1, no. 1, pp. 51–56.

S. R. Widianto, M. S. Maulana, E. B. Pratama, Y. Firmansyah, and N. Nurmalasari, “Python gmail dictionary attack using wordlist,” in AIP Conference Proceedings, 2023, vol. 2714, no. 1.

L. Pasyarani, “Revitalisasi Akuntansi dengan Penerapan Kecerdasan Buatan (Artificial Intelligence),” J. Ilmu Data, vol. 3, no. 2, pp. 1–14, 2023.

A. A. Fauzi et al., PEMANFAATAN TEKNOLOGI INFORMASI DI BERBAGAI SEKTOR PADA MASA SOCIETY 5.0. PT. Sonpedia Publishing Indonesia, 2023.

B. Kwintiana et al., DATA SCIENCE FOR BUSINESS: Pengantar & Penerapan Berbagai Sektor. PT. Sonpedia Publishing Indonesia, 2023.

Paulus Libu Lamawitak and Emilianus Eo Kutu Goo, “Pengaruh Fraud Diamond Theory Terhadap Kecurangan (Fraud) Pada Koperasi Kredit Pintu Air,” J. Penelit. Ekon. Akunt., vol. 5, no. 1, pp. 56–67, 2021, doi: 10.33059/jensi.v5i1.3620.

S. R. Widianto, S. Y. Sudiro, I. Suwandi, and L. Leiliawati, “Database Management System on Raw Material Transaction System Case Study: Sabana Fried Chicken,” J. Mantik, vol. 4, no. 3, pp. 1722–1727, 2020.

A. Gebo, P. W. Aditama, I. B. G. Sarasvananda, and I. P. H. Permana, “SISTEM INFORMASI LAPORAN KEUANGAN PADA SMK NEGERI 1 ENDE BERBASIS WEB,” J. Krisnadana, vol. 1, no. 3, pp. 15–25, 2022.

Erlina F. Santika, “Jumlah Emiten di Bursa Efek Indonesia Kerap Meningkat Sepanjang Januari-Mei 2023,” databoks.katadata.co.id, 2023.

M. Maulana, T. Tursina, and R. Septiriana, “Prediksi Jumlah Penduduk menggunakan Metode Fuzzy Time Series,” J. Impresi Indones., vol. 2, no. 3, pp. 206–216, 2023.

R. Septiriana and A. Perwitasari, “Prediction Of The Number Of Course Participants Using Random Forest Regression Algorithm,” J. Mantik, vol. 6, no. 3, pp. 3393–3399, 2022.

T. D. Puspitasari, R. Septiriana, and V. Ayu, “Sistem Pakar Identifikasi Penyakit Mata Menggunakan Metode Dempster-Shafer,” SEMNASKIT 2015, 2018.

P. W. Rahayu et al., Buku Ajar Data Mining. PT. Sonpedia Publishing Indonesia, 2024.

R. Novaria, A. Alfiah, M. Khaddafi, T. Tukino, and I. G. I. Sudipa, “Predicted demand for 3 kg LPG gas in each provinces area in Indonesia,” J. Info Sains Inform. dan Sains, vol. 14, no. 01, pp. 125–136, 2024.

A. Afifuddin and L. Hakim, “Deteksi Penyakit Diabetes Mellitus Menggunakan Algoritma Decision Tree Model Arsitektur C4. 5,” J. Krisnadana, vol. 3, no. 1, pp. 25–33, 2023.

Y.-J. Chen, W.-C. Liou, Y.-M. Chen, and J.-H. Wu, “Fraud detection for financial statements of business groups,” Int. J. Account. Inf. Syst., vol. 32, pp. 1–23, 2019.

B. Natasia, “Analisis Faktor-Faktor Yang Mempengaruhi Terjadinya Fraud Dalam Pelaporan Keuangan,” J. El-Riyasah, vol. 11, no. 1, pp. 80–92, 2020.

D. Coderre, Fraud analysis techniques using ACL. John Wiley & Sons, 2009.

H. Herbenita, A. Rahmawati, and A. Surwanti, “Potential of Fraud Financial Statements: The Fraud Triangle,” Cent. Asian J. Innov. Tour. Manag. Financ., vol. 3, no. 10, pp. 201–212, 2022.

M. M. A. Saleh, M. Aladwan, O. Alsinglawi, and M. O. Salem, “Predicting fraudulent financial statements using fraud detection models,” Acad. Strateg. Manag. Journal, suppl. Spec., vol. 20, no. 3, pp. 1–17, 2021.

A. Haqq and G. S. Budiwitjaksono, “Fraud pentagon for detecting financial statement fraud,” J. Econ. Business, Account. Ventur., vol. 22, no. 3, pp. 319–332, 2019.

M. B. Ibrahim et al., METODE PENELITIAN BERBAGAI BIDANG KEILMUAN (Panduan & Referensi). PT. Sonpedia Publishing Indonesia, 2023.

H. Kurniawan et al., TEKNIK PENULISAN KARYA ILMIAH: Cara membuat Karya Ilmiah yang baik dan benar. PT. Sonpedia Publishing Indonesia, 2023.

A. Bănărescu, “Detecting and preventing fraud with data analytics,” Procedia Econ. Financ., vol. 32, pp. 1827–1836, 2015.

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Published

2024-02-01

How to Cite

Septiriana, R., Widianto, S. R., & Darma, P. E. (2024). Application of artificial intelligence in the prevention of fraud in financial statements . Jurnal Ekonomi, 13(01), 1417–1423. Retrieved from https://ejournal.seaninstitute.or.id/index.php/Ekonomi/article/view/3960