FİNANSAL TABLO HİLESİ RİSKİ TAŞIYAN ŞİRKETLERİN VERİ MADENCİLİĞİ İLE BELİRLENMESİ

Abstract

Finansal tablo hilesi, şirketlerin finansal tablolarındaki verileri kendi çıkarları doğrultusunda değiştirerek yayınlamalarıdır. Kurumlara, paydaşlara ve ekonomik yapıya ciddi zararlar veren finansal tablo hilelerinin tespit edilmesi önemli bir problemdir. Bunun için çeşitli denetim mekanizmaları bulunmaktadır. Ancak zaman içerisinde geliştirilebilecek hile yöntemlerine karşı yenilikçi denetim yöntemlerine ihtiyaç duyulmaktadır. Veri madenciliği, finansal tablo hilelerinin tespitinde umut vadeden bir alandır. Veri madenciliğinin sınıflandırma analizinde sınıflandırma metotlarıyla mevcut verilerden örüntüler elde edilir ve bunlar görülmemiş birimlerin sınıflandırılmasında kullanılır. Bu çalışmada veri madenciliğinin sınıflandırma metotları ile finansal tablo hilesi riski taşıyan şirketlerin tespiti üzerine bir araştırma yapılmıştır. Veriler Borsa İstanbul’da 2014-2018 arasında işlem gören şirketlerin yayınladıkları finansal tablolardan elde edilmiştir. İlk olarak yedi sınıflandırılma metodu kullanılmış, en başarılı üçü seçilmiştir. Sonraki aşamada başarım değerlerinin geliştirilmesi amacıyla hiper parametre optimizasyonu yapılmıştır. Sınıflandırma metotlarından K-Nearest Neighbor ile yüzde 91,73, Random Forest ile yüzde 90,51 ve XGBoost ile yüzde 90,37 doğruluk oranlarına ulaşılmış, en iyi tahmin oranı K-Nearest Neighbor ile elde edilmiştir. Son kısımda rasgele alt örnekleme yöntemiyle yapılan karşılaştırmalarda da en iyi performans değerleri K-Nearest Neighbor ile elde edilmiştir.

Keywords

• Finansal Tablo Hilesi
• Veri Madenciliği
• Sınıflandırma

IDENTIFYING THE COMPANIES WITH THE RISK OF FINANCIAL STATEMENT FRAUD BY DATA MINING

Abstract

Financial statement fraud is when companies change and publish the data in their financial statements in line with their interests. Detecting financial statement fraud that causes serious damage to organizations, stakeholders, and the economic structure is an important problem. There are various control mechanisms for this. However, there is a need for innovative control methods against new fraud methods that may be developed over time. Data mining is a promising field for detecting financial statement fraud. In the classification analysis of data mining, patterns are obtained from existing data with classification methods, and these are used in the classification of unseen units. In this study, research was carried out on the identification of companies with the risk of financial statement fraud through the classification methods of data mining. The data were obtained from the financial statements published by companies traded in Borsa Istanbul between 2014 and 2018. Seven classification methods were used first, and the three most successful ones were selected. In the next stage, hyperparameter optimization was carried out to improve the performance values. Accuracy rates of 91.73 percent with K-Nearest Neighbor, 90.51 percent with Random Forest, and 90.37 percent with XGBoost were obtained from the classification methods, and the best prediction rate was obtained with K-Nearest Neighbor. In the last part, the best performance values were also obtained with K-Nearest Neighbor in the comparisons using random sub-sampling method.

Keywords

• Financial Statement Fraud
• Data Mining
• Classification

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