TY - GEN
T1 - Data mining application to detect financial fraud in Indonesia's public companies
AU - Rizki, Adila Afifah
AU - Prajitno, Isti Surjandari
AU - Wayasti, Reggia Aldiana
PY - 2018/1/12
Y1 - 2018/1/12
N2 - 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.
AB - 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.
KW - artificial neural network
KW - classification
KW - data mining
KW - financial statement fraud
KW - support vector machine
UR - http://www.scopus.com/inward/record.url?scp=85046677693&partnerID=8YFLogxK
U2 - 10.1109/ICSITech.2017.8257111
DO - 10.1109/ICSITech.2017.8257111
M3 - Conference contribution
T3 - Proceeding - 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
SP - 206
EP - 211
BT - Proceeding - 2017 3rd International Conference on Science in Information Technology
A2 - Drezewski, Rafal
A2 - Chakraborty, Goutam
A2 - Nazir, Shah
A2 - Riza, Lala Septem
A2 - Hashim, Ummi Raba'ah
A2 - Wibawa, Aji Prasetyo
A2 - Wihardi, Yaya
A2 - Pranolo, Andri
A2 - Junaeti, Enjun
A2 - Horng, Shi-Jinn
A2 - Lim, Heui Seok
A2 - Hernandez, Leonel
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Science in Information Technology, ICSITech 2017
Y2 - 25 October 2017 through 26 October 2017
ER -