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.

Purpose -This study aims to determine the optimal renewable energy investment project providing a guideline to the investors in decision making process. Methodology -This study presents a comprehensive and solid mathematical approach considering the assessment of the ambiguities in the preferences of the decision maker for selection of the optimal renewable energy investment project via fuzzy analytic network process (FANP). FANP captures vagueness along with uncertainties in the evaluation. Findings -After FANP method had been implemented for the considered problem, Hydropower with 31% of importance is selected as optimum renewable energy investment project for the firm. Conclusion-This study provides a realistic assessment of energy resources and the consideration of the ambiguities presented in the preferences of the decision maker.

Many studies have used different financial ratios for financial accounting fraud detection. This study focuses on multi criteria decision-making (MCDM) for ranking financial ratios in detecting financial accounting fraud using interval-valued spherical fuzzy sets (IVSFS) and single-valued spherical fuzzy sets (SVSFS) to overcome uncertainties in decision-making process of financial analysts. This study proposes an integrated Analytic Hierarchy Process (AHP) and Multi-Objective Optimization by a Ratio Analysis plus the Full Multiplicative Form (MULTIMOORA) approach using IVSFS and SVSFS. Comparative results are obtained and discussed in prioritization of financial ratios for both IVSFS and SVSFS.

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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.