Open AccessDetection of Fraudulent Financial Statement based on Ratio Analysis in Indonesia Banking using Support Vector Machine
Author(s) -
Yuliant Sibaroni,
Muhammad Novario Ekaputra,
Sri Suryani Prasetiyowati
Publication year - 2020
Publication title -
join (jurnal online informatika)
Language(s) - English
Resource type - Journals
eISSN - 2528-1682
pISSN - 2527-9165
DOI - 10.15575/join.v5i2.646
Subject(s) - support vector machine , financial ratio , financial statement , return on equity , return on assets , computer science , capital adequacy ratio , finance , business , accounting , data mining , actuarial science , machine learning , profit (economics) , audit , economics , stock exchange , microeconomics
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.