HİLELİ FİNANSAL TABLOLARIN TESPİTİNDE VERİ MADENCİLİĞİ UYGULAMALARI: MEVCUT ARAŞTIRMA EĞİLİMLERİNİN İNCELENMESİ (2006-2024)

Yıl 2025, Sayı: 33, 333 - 355, 03.09.2025

Tuğrul Kandemir , Zafer Kardeş

Öz

Finansal piyasaların giderek karmaşıklaşması ve mevcut verilerin katlanarak artması, hileli finansal tablo tespitini kritik ve zor bir konu haline getirmiştir. Büyük hacimli verilerden ve finansal verilerin karmaşıklığından ilgili bilgiyi çıkarıp hileli finansal tablo tespit etmeye olanak sağlayan çeşitli veri madenciliği yöntemleri geliştirilmiştir. Son yıllarda veri madenciliği yöntemleri, hileli finansal tablo tespiti için en güvenilir yöntemlerden biri haline gelmiştir. Bu araştırmanın amacı, hileli finansal tablo tespitinde veri madenciliğinin uygulanmasına ilişkin mevcut araştırmalar hakkında bir inceleme sağlamak ve bulguları karşılaştırmaktır. Bu araştırmada bilimsel dergilerde yayınlanan, hileli finansal tablo tespitinde veri madenciliği yöntemlerini uygulayan araştırmaların nicelik açısından değerlendirilmesiyle birlikte araştırmacılara ve uygulayıcılara bir veri tabanı oluşturulması amaçlanmıştır. Araştırma sonucunda, en yaygın kullanılan veri madenciliği yöntemlerinin sırasıyla karar ağacı, destek vektör makineleri, lojistik regresyon ve yapay sinir ağı olduğu görülmüştür. Araştırmada en iyi performans gösteren yöntemlerin ise lojistik regresyon, hibrit modeller, sinir ağı ve XGBoost algoritmasının olduğu ve ele alınan araştırmaların %56,10’unda örneklem büyüklerinin 1.000’den az olduğu tespit edilmiştir.

Anahtar Kelimeler

Hile Tespiti , Hileli Finansal Tablolar , Veri Madenciliği , Makine Öğrenimi , Literatür Taraması

Etik Beyan

Bu çalışmada, Araştırma ve Yayın Etiğine uyulmuştur, çıkar çatışması bulunmamaktadır ve çalışma kapsamında finansal destek alınmamıştır.

DATA MINING PRACTICES IN THE DETECTION OF FRAUDULENT FINANCIAL STATEMENTS: A REVIEW OF THE CURRENT RESEARCH TRENDS (2006-2024)

Öz

The increasing complexity of financial markets and the exponential increase in available data have made fraudulent financial statement detection a critical and difficult issue. Various data mining methods have been developed that make it possible to extract relevant information from large amounts of data and the complexity of financial data in order to recognize fraudulent financial statements. In recent years, data mining methods have become one of the most reliable methods for fraudulent financial statement detection. This study aims to provide a review of existing research on the application of data mining in fraudulent financial statement detection and compare the findings. This study aims to quantitatively evaluate research published in scientific journals that applies data mining methods to fraudulent financial statement detection and to create a database for researchers and practitioners. The study revealed that the most frequently used data mining methods were decision trees, support vector machines, logistic regression, and artificial neural networks. It was also detected that the best performing methods were logistic regression, hybrid models, neural network and XGBoost algorithm, and 56.10% of the studies had sample sizes less than 1,000.

Anahtar Kelimeler

Fraud Detection , Fraudulent Financial Statements , Data Mining , Machine Learning , Literature Review

Etik Beyan

This paper complies with Research and Publication Ethics, has no conflict of interest to declare, and has received no financial support.

Kaynakça

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