Data mining application to detect financial fraud in Indonesia's public companies

Adila Afifah Rizki, Isti Surjandari Prajitno, Reggia Aldiana Wayasti

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Citations (Scopus)

Abstract

Association of Certified Fraud Examiners explains that there are 3 types of occupational fraud: financial statement fraud, asset misappropriation and corruption. Among these three, financial statement fraud caused the biggest losses, which amounted to $ 1,000,000 in 2014. Financial statement has important role as an indicator of the success of a company, also for depicting the overall condition of the company, deciding company's stock price, and determining whether the company could be granted a loan or not. Given its important role, many cases of fraud occur. Audit activities are conducted to minimize losses, but the number of available auditors is limited, and the time required for traditional audit is quite long. Therefore, an effective model of financial fraud detection is needed to help auditors in analyzing financial statements. Data mining algorithms, support vector machine (SVM) and artificial neural network (ANN), were applied in this study. The results of this study give insight to the auditor that significant indicators in detecting financial fraud are profitability and efficiency. Feature selection improves SVM algorithm accuracy to 88.37%. ANN produces the highest accuracy, 90.97%, for data without feature selection.

Original languageEnglish
Title of host publicationProceeding - 2017 3rd International Conference on Science in Information Technology
Subtitle of host publicationTheory and Application of IT for Education, Industry and Society in Big Data Era, ICSITech 2017
EditorsRafal Drezewski, Goutam Chakraborty, Shah Nazir, Lala Septem Riza, Ummi Raba'ah Hashim, Aji Prasetyo Wibawa, Yaya Wihardi, Andri Pranolo, Enjun Junaeti, Shi-Jinn Horng, Heui Seok Lim, Leonel Hernandez
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages206-211
Number of pages6
ISBN (Electronic)9781509058662
DOIs
Publication statusPublished - 12 Jan 2018
Event3rd International Conference on Science in Information Technology, ICSITech 2017 - Bandung, Indonesia
Duration: 25 Oct 201726 Oct 2017

Publication series

NameProceeding - 2017 3rd International Conference on Science in Information Technology: Theory and Application of IT for Education, Industry and Society in Big Data Era, ICSITech 2017
Volume2018-January

Conference

Conference3rd International Conference on Science in Information Technology, ICSITech 2017
CountryIndonesia
CityBandung
Period25/10/1726/10/17

Keywords

  • artificial neural network
  • classification
  • data mining
  • financial statement fraud
  • support vector machine

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