IMPROVING FRACTALS FINANCIAL CREDIT RISK EVALUATION BASED ON DEEP LEARNING TECHNIQUES AND BLOCKCHAIN-BASED ENCRYPTION

Fadoua Kouki, Hanan Abdullah Mengash, Nuha Alruwais, Achraf Ben Miled, Jawhara Aljabri and Ahmed S. Salama
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Fadoua Kouki: Department of Financial and Banking Sciences, Applied College at Muhail Aseer, King Khalid University, Saudi Arabia
Hanan Abdullah Mengash: Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh 11671, Saudi Arabia
Nuha Alruwais: Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, Saudi Arabia, P. O. Box 22459, Riyadh 11495, Saudi Arabia
Achraf Ben Miled: Department of Computer Science, College of Science, Northern Border University, Arar 73213, Saudi Arabia
Jawhara Aljabri: Department of Computer Science, University College in Umluj, University of Tabuk, Saudi Arabia
Ahmed S. Salama: Department of Electrical Engineering, Faculty of Engineering and Technology, Future University in Egypt, New Cairo 11845, Egypt

FRACTALS (fractals), 2025, vol. 33, issue 02, 1-14

Abstract: Predicting a client’s affluence is essential in financial services. This task is the unity of the most important danger factors in groups and additional economic institutions. Typically, credit risk evaluation relies on black box models. However, these models often need to clarify the hidden information within the data. Moreover, few clear models focus on being easy to understand and accessible. This paper proposes a fractal credit risk assessment model that uses deep techniques like self-attention generative adversarial networks (SA-GAN) and deep multi-layer perceptron (DMLP). We use blockchain technology with the Brakerski–Gentry–Vaikuntanathan (BGV) encryption method to bolster safekeeping. Additionally, the scheme is designed for the Edge-of-things network, enabling communication through a LoRaWAN server. The proposed solution was tested on the German retail credit dataset. We assessed its performance using accuracy, F1 score, precision, and recall as metrics. Notably, our hybrid deep model, which combines SA-GAN with DMLP, achieved an impressive accuracy of 97.8% — outperforming existing methods in works.

Keywords: Fractals Credit Risk Assessment; Blockchain; Deep Learning; Edge Computing; Neural Networks (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0218348X2540033X

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