Homomorphic Encryption in Finance: Training and Inference on Encrypted Data with Concrete ML

Authors : Ensar Yilmaz, Burak Aktürk, Didem Civelek, Tolga Büyüktanır, Kazım Yıldız

Pages : 64-77

Doi:10.35414/akufemubid.1686923

View : 44 | Download : 402

Publication Date : 2026-01-19

Article Type : Research Paper

Abstract :Although machine learning is a significant technology that offers useful insights into financial analysis procedures, the actual use of these technologies is restricted by data privacy concerns. A completely homomorphic encryption-based approach presented in this paper to train machine learning models and generate predictions while protecting the privacy of business financial data. The goal of the suggested approach is to use encrypted data to generate predictions while training machine learning models on open data.During the training stage, financial ratios from publicly accessible sources were used to train machine learning models on open data and prepare them for encrypted inference.The client-side encrypted financial information is transmitted to the server during the inference phase, and the prediction procedure was conducted in an encrypted. During the inference phase, the client-side financial data was encrypted and transmitted to the server. The prediction process was also encrypted, and the outcome was only decrypted on the client side. The Altman Z-Score, L-Score, and Zmijewski Score models were employed in the research, and the encrypted models\\\' accuracy values (R2: 0.68-0.98) performed comparably to the open models. The accuracy levels of the encrypted models have produced results that are quite similar to those of the open models in experiments utilizing models predicting a company\\\'s status, such as the Altman Z-Score, L-Score, and Zmijewski Score. Performance metrics including inference and training times have also been examined. The findings obtained indicate that this approach developed with Concrete ML provides a secure and effective solution in financial applications where privacy is critical.
Keywords : Homomorfik Şifreleme, Finansal Risk Tahmini, Concrete ML Çerçevesi, Şifrelenmiş Çıkarım, Finansal Zorluk Modelleri

ORIGINAL ARTICLE URL