VERİ MADENCİLİĞİ VE MUHASEBEDE KULLANIMI: MUHASEBE DOLANDIRICILIĞININ TESPİTİNDE EN ÇOK KULLANILAN YÖNTEMLER

Yıl 2025, Cilt: 12 Sayı: 4, 318 - 345, 31.10.2025

Fatih Ömür Binici

Öz

Günümüzde teknolojinin yaygınlaşması ve gelişmesiyle birlikte işletmeler, analiz edilmesi ve yorumlanması gereken muazzam miktarda finansal veri üretmektedir. Üretilen bu büyük miktardaki verilerin analizi, geleneksel yöntemlerle mümkün olmamaktadır. Bu nedenle işletmeler, geleneksel yöntemler yerine Veri Madenciliği (VM) yöntemlerine yönelmiştir. VM, büyük veri tabanlarında istatistiksel olarak güvenilir, daha önce bilinmeyen, eyleme dönüştürülebilir içgörüler ve ilginç desenler elde etmek için istatistik, matematik, yapay zeka ve makine öğrenmesi gibi teknikleri kullanan kapsamlı bir süreçtir. İşletmelerde VM'nin kullanım alanları çok çeşitli olmakla birlikte, yoğun olarak muhasebe ve finans alanında dolandırıcılık faaliyetlerinin tespitinde kullanılmaktadır. Bu yoğun kullanım nedeniyle, verilerin analizinde seçilecek yöntem ve uygulamaların belirlenmesi büyük önem taşımaktadır.
Bu doğrultuda çalışmada, muhasebedeki veri madenciliği uygulamalarına ilişkin alan yazınını incelemek ve muhasebe dolandırıcılığının tespiti için en yaygın kullanılan veri madenciliği tekniklerine dair kavramsal bir çerçeve sunmak amaçlanmıştır. Belirlenen amaç doğrultusunda, alan yazınında yer alan sistematik literatür taramaları birleştirilerek birincil kaynaklardan toplanan veriler “Şemsiye İncelemesi (Umbrella Review)” yöntemiyle analiz edilmiştir. Yapılan analiz sonucunda, VM yöntemlerinden “Regresyon (Logit/Logistic/Probit)” yönteminin, muhasebe dolandırıcılığının tespitinde en çok kullanılan yöntem olduğu sonucuna ulaşılmıştır. En az kullanılan yöntemlerin ise Otoenkoder (Autoencoder, AE), REP (Reduced Error Pruning) ve Stokastik Gradyan İnişi (Stochastic Gradient Descent, SGD) olduğu tespit edilmiştir.

Anahtar Kelimeler

Muhasebe , Veri Madenciliği , Muhasebe Dolandırıcılığı

DATA MINING AND ITS USE IN ACCOUNTING: THE MOST COMMONLY USED METHODS IN DETECTING ACCOUNTING FRAUD

Öz

In today's world, businesses generate large amounts of financial data that must be analyzed and interpreted due to technological advances and proliferation. Traditional methods cannot analyze this massive data set. As a result, businesses have turned to data mining (DM) techniques. DM is an extensive process that employs methods such as statistics, mathematics, artificial intelligence, and machine learning to extract reliable, previously unknown insights and patterns from large databases, informing decision-making. While DM has various applications in business, it is especially prevalent in accounting and finance for fraud detection. Due to this widespread use, selecting the right data analysis methods and applications is crucial.
Against this backdrop, the present study aims to review the literature on data mining applications in accounting and to present a conceptual framework for the most commonly used data mining techniques for detecting accounting fraud. To achieve this, a systematic review of the literature was conducted, and data collected from primary sources were analyzed using the 'umbrella review' method. The analysis found that the 'Regression (Logit/Logistic/Probit)' method is the most frequently used for this purpose. The least common methods identified were Autoencoder (AE), Reduced Error Pruning (REP), and Stochastic Gradient Descent (SGD).

Anahtar Kelimeler

Accounting , Data Mining , Accounting Fraud

Kaynakça

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