Detection of Fraudulent Financial Statement based on Ratio Analysis in Indonesia Banking using Support Vector Machine

Authors

  • Yuliant Sibaroni Faculty of Informatics, Telkom University, Indonesia http://orcid.org/0000-0002-9275-8972
  • Muhammad Novario Ekaputra Faculty of Informatics, Telkom University, Indonesia
  • Sri Suryani Prasetiyowati Faculty of Informatics, Telkom University, Indonesia

DOI:

https://doi.org/10.15575/join.v5i2.646

Keywords:

Classification, Feature, Financial Ratio, Fraudulent, Ratio Analysis

Abstract

This study proposes the use of ratio analysis-based features combined with the SVM classifier to identify fraudulent financial statements. The detection method used in this study applies a data mining classification approach. This method is expected to replace the expert in forensic accounting in identifying fraudulent financial statements that are usually done manually. The experimental results show that the proposed classifier model and ratio analysis-based features provide more than 90% accuracy results where the optimal number of features based on ratio analysis is 5 features, namely Capital Adequacy Ratio (CAR), (ANPB) to total earning assets and non-earning assets (ANP), Impairment provision on earning assets (CKPN) to earning assets, Return on Asset (ROA), and Return on Equity (ROE). The contribution of the study is to complement the research of fraudulent financial statements detection where the classifier method used here is different compare to other research. The selection of banking cases in Indonesia is also unique in this research which distinguishes it from other research because the financial reporting standards in each country can be different. 

Author Biography

Yuliant Sibaroni, Faculty of Informatics, Telkom University

Dosen di Fakultas Informatika , Telkom University lulusan dari UGM (S1), ITB(S2) dan  STEI-ITB (S3-2020) dg bidang Text Mining (Classification, Information extraction)

References

A. Lako, Laporan Keuangan & Konflik Kepentingan. Yogyakarta: Amara Books, 2007.

A. K. Rowland Bismark Fernando Pasaribu, “Fraud Laporan Keuangan Dalam Perspektif Fraud Triangle,†J. Ris. Akunt. dan Keuang. Fak. Bisnis, vol. 14, no. 1, 2018.

K. M. Fanning and K. O. Cogger, “Neural network detection of management fraud using published financial data,†Intellegent Syst. Accounting, Financ. Manag., 1998.

M. M. Clayton, J. C. Moorman, J. Wilkinson, M. Shackell, and G. Schaffer, “Data Mining : Computer-Adided Forensic Accounting Investigation Techniques,†in A Guide To Forensic Accounting Investigation, Second Edi., Hoboken: John Wiley & Sons, Inc., 2005, p. 554.

S. Prasmaulida, “Financial Statement Fraud Detection Using Perspective of Fraud Triangle Adopted By Sas No. 99,†Asia Pacific Fraud J., vol. 1, no. 2, p. 317, 2016, doi: 10.21532/apfj.001.16.01.02.24.

A. Arfiyadi and I. Anisykurlillah, “The Detection of Fraudulent Financial Statement with Fraud Diamond Analysis,†Account. Anal. J., vol. 5, no. 3, 2016.

N. K. A. Yulistyawati, I. M. S. Suardikha, and I. P. Sudana, “The analysis of the factor that causes fraudulent financial reporting with fraud diamond,†J. Akunt. Audit. Indones., vol. 23, no. 1, pp. 1–10, 2019, doi: 10.20885/jaai.vol23.iss1.art1.

M. Yesiariani and I. Rahayu, “Jurnal Akuntansi & Auditing Indonesia Deteksi financial statement fraud : Pengujian dengan fraud diamond,†vol. 21, no. 1, 2017.

Q. Deng and G. Mei, “Combining self-organizing map and K-means clustering for detecting fraudulent financial statements,†IEEE Int. Conf. Granul. Comput., 2009.

D. Yue, X. Wu, and N. Shen, “Logistic regression for detecting fraudulent financial statement of listed companies in China,†2009 Int. Conf. Artif. Intell. Comput. Intell., 2009.

Q. Deng, “Detection of fraudulent financial statements based on Naïve Bayes classifier,†5th Int. Conf. Comput. Sci. Educ., 2010.

E. Kirkos, C. Spathis, and Y. Manolopoulos, “Data Mining techniques for the detection of fraudulent financial statements,†Expert Syst. Appl., vol. 32, no. 4, pp. 995–1003, 2007, doi: 10.1016/j.eswa.2006.02.016.

M. L. Khodra, D. H. Widyantoro, E. A. Aziz, and B. R. Trilaksono, “Free Model of Sentence Classifier for Automatic Extraction of Topic Sentences,†vol. 5, no. 1, pp. 17–34, 2011.

S. Teufel and A. Athar, “Detection of Implicit Citations for Sentiment Detection,†Proc. ACL-12 Work. Discov. Struct. Sch. Discourse, Jeju Island, South Korea, 2012, no. July, pp. 18–26, 2012.

Y. Sibaroni, D. H. Widyantoro, and M. L. Khodra, “Extend Relation Identification in Scientific Papers Based On Supervised Machine Learning,†2016 Int. Conf. Adv. Comput. Sci. Inf. Syst. (ICACSIS 2016), 2016.

S. Chen, “Detection of fraudulent financial statements using the hybrid data mining approach,†Springerplus, vol. 5, no. 1, pp. 1–16, 2016, doi: 10.1186/s40064-016-1707-6.

Downloads

Published

2020-12-03

Issue

Section

Article

Citation Check

Most read articles by the same author(s)