THE POWER OF MACHINE LEARNING MODELS IN EARLY DETECTION OF FRAUDULENT FINANCIAL STATEMENTS: THE EXAMPLE OF BORSA ISTANBUL

Abstract

Detecting and preventing fraudulent financial statements is crucial to maintaining the reliability of financial markets, as such statements undermine stakeholders and disrupt healthy market functioning. This study develops an artificial intelligence–supported model to detect fraudulent financial statements of companies listed on Borsa Istanbul. Using financial and non-financial data, Random Forest, Naive Bayes, and K-Star classification analyses were applied to firms identified as preparing fraudulent statements according to Capital Markets Board bulletins between 01.01.2022 and 01.01.2025. Results indicate that the K-Star algorithm achieved 99% accuracy in detecting fraudulent statements one period in advance, compared with 92% for Random Forest and 62% for Naive Bayes. Specifically, K-Star classified fraudulent firms with 100% accuracy and non-fraudulent firms with 98% accuracy. These results highlight that combining financial and non-financial indicators offers a novel and effective approach to fraud detection and provides significant contributions to the literature.

Keywords

• Fraudulent financial statement
• Financial fraud detection
• Machine learning
• Borsa Istanbul

Project Number

yok

Ethical Statement

No ethical clearance required.

THE POWER OF MACHINE LEARNING MODELS IN EARLY DETECTION OF FRAUDULENT FINANCIAL STATEMENTS: THE EXAMPLE OF BORSA ISTANBUL

Abstract

Finansal tabloların hileli düzenlenmesinin tespiti ve önlenmesi, finansal piyasaların güvenilirliğinin korunması açısından kritik öneme sahiptir. Hileli tablolar, tüm paydaşlara zarar vermekte ve piyasaların sağlıklı işleyişini bozmaktadır. Bu çalışma, Borsa İstanbul’da işlem gören şirketlerin hileli finansal tablolarının tespitine yönelik yapay zekâ destekli bir model geliştirmeyi amaçlamaktadır. Bu kapsamda, 01.01.2022–01.01.2025 döneminde Sermaye Piyasası Kurulu bültenlerine göre hileli finansal tablo hazırladığı belirlenen şirketler üzerinde finansal ve finansal olmayan verilerle Random Forest, Naive Bayes ve K-Star sınıflandırma analizleri uygulanmıştır. Bulgular, K-Star algoritmasının hileli finansal tabloları bir dönem önceden %99 doğrulukla tespit ettiğini; Random Forest’ın %92, Naive Bayes’in ise %62 doğruluk sağladığını göstermektedir. En yüksek performansa sahip K-Star algoritması, hileli şirketleri %100, hilesizleri ise %98 doğrulukla sınıflandırmıştır. Bu sonuçlar, finansal ve finansal olmayan verilerin birlikte kullanımının hileli finansal tabloların tespitinde yenilikçi ve etkili bir yaklaşım sunduğunu ortaya koymaktadır.

Keywords

• hileli finansal tablo
• finansal hilelerin tesbiti
• makine öğrenmesi
• Borsa İstanbul

Project Number

yok

Ethical Statement

Etik kurul izni gerekmemektedir.

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