Uluslararası Finansal Raporlama Standartlarının KOBİ’ler Açısından Değerlendirilmesi – Türkiye Uygulaması

Karahan, Murat; Iğde, Medet; Özbezek, Dilek

doi:10.21547/jss.299362

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…The implementation of auditing is not obligatory for these SMEs in Turkey. Companies, which are subject to audit, are specified by the Cabinet Decree, and most of the SMEs do not comply regulations which are determined by the Cabinet Decree [23]. For these reasons, Turkish SMEs are greatly exposed to fraud risks and creditor banks are facing big challenges to deal with financial accounting fraud.…”

Section: Introductionmentioning

confidence: 99%

Comparing performances and effectiveness of machine learning classifiers in detecting financial accounting fraud for Turkish SMEs

Hamal¹,

Şenvar²

2021

IJCIS

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Turkish small-and medium-sized enterprises (SMEs) are exposed to fraud risks and creditor banks are facing big challenges to deal with financial accounting fraud. This study explores effectiveness of machine learning classifiers in detecting financial accounting fraud assessing financial statements of 341 Turkish SMEs from 2013 to 2017. The data are obtained from one of the leading creditor banks of Turkey. Highly imbalanced classes of 1384 nonfraudulent cases and 321 fraudulent cases (by 122 firms) are detected thus sampling techniques are used to mitigate class imbalance problem. Research methodology consists of two stages. First stage is data preprocessing wherein financial ratio calculation, feature selection methods for defining financial ratios with the greatest impact on fraudulent financial statements and two sampling methods of Synthetic Minority Oversampling Technique (SMOTE) as oversampling and undersampling are performed, respectively. Second stage is performance evaluation and comparison of classifiers wherein seven different classifiers (support vector machine, Naive Bayes, artificial neural network, K-nearest neighbor, random forest, logistic regression, and bagging) are executed and compared by using performance metrics. Classifiers are also compared without using any feature selection and/or sampling techniques. Results reveal that random forestwithout feature selection-oversampling model outperforms all other models.

show abstract

Section: Introductionmentioning

confidence: 99%

Comparing performances and effectiveness of machine learning classifiers in detecting financial accounting fraud for Turkish SMEs

Hamal¹,

Şenvar²

2021

IJCIS

View full text Add to dashboard Cite

show abstract

Farklı Sektörlerden Türk Küçük ve Orta Büyüklükteki İşletmelerin(KOBİler) Hileli Finansal Tablolarının İncelenmesi

Şenvar

Hamal

2022

EJOSAT

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Financial statement fraud has negative effects on sustainable financial development of businesses and industries. In this sudy, Turkish SMEs from different sectors have been examined in terms of fraud detection in financial statements. Banks, which assess the credit demands of SMEs, must be vigilant and innovative to combat financial accounting fraud. From these standpoints, comprehensive comparison study is conducted by examining 341 Turkish SMEs' financial statements from different sectors (manufacturing, construction, transportation, agricultural). The data of these SMEs are collected from one of the largest banks, based on total assets, in Turkey in which the bank provide them loans for funding. Results show that firms in construction sector mostly manipulate Cash (100), Due from Shareholders (131) financial accounts more than firms in other sectors (with rate of 56 %). This study can be guideline to comprehend on sectoral basis which financial accounts are mostly used in fraudulent financial reporting in which it can be useful to reduce fraud risks for SMEs along with to protect and prevent against fraud for all players in financial reporting system.

show abstract

Uluslararası Finansal Raporlama Standartlarının KOBİ’ler Açısından Değerlendirilmesi – Türkiye Uygulaması

Cited by 2 publications

References 24 publications

Comparing performances and effectiveness of machine learning classifiers in detecting financial accounting fraud for Turkish SMEs

Comparing performances and effectiveness of machine learning classifiers in detecting financial accounting fraud for Turkish SMEs

Farklı Sektörlerden Türk Küçük ve Orta Büyüklükteki İşletmelerin(KOBİler) Hileli Finansal Tablolarının İncelenmesi

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