Multi-Level Classification of Audit Opinions Using Ensemble Learning Methods with Encrypted Financial Data

Authors : Elif Nur Kucur, Burak Aktürk, Ensar Yilmaz, Tolga Büyüktanır, Kazım Yıldız

Pages : 269-282

Doi:10.17671/gazibtd.1708959

View : 142 | Download : 134

Publication Date : 2025-07-31

Article Type : Research Paper

Abstract :Independent audit reports play a crucial role in assessing the financial reliability of companies. Auditors base their opinions on the accuracy and consistency of financial statements and their underlying components. This study aims to automatically predict audit opinions by leveraging financial ratios derived from financial statements, as well as well-known financial risk scores such as Altman-Z, Springate, and Zmijewski. Classification was performed using XGBoost and Random Forest algorithms. Considering data privacy requirements, the modeling process was implemented using the Concrete ML library, which supports homomorphic encryption, thereby preserving the confidentiality of financial data. A hierarchical classification approach was adopted further to subdivide unqualified audit opinions into more detailed sub-classes, enhancing interpretability. Experimental results show that the proposed model achieves strong performance in terms of both accuracy and F1 score. The developed system is expected to serve as a predictive, systematic, and privacy-aware decision support tool for auditors and other stakeholders prior to the formal audit process.
Keywords : mahremiyet koruyan makine öğrenmesi, bağımsız denetim görüşü sınıflandırma, hiyerarşik sınıflandırma, finansal oranlar, topluluk öğrenmesi

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