Title: Privacy-preserving legal credit risk assessment framework driven by generative AI with interpretability analysis

Authors: Di Teng

Addresses: College of Humanities and Law, Harbin Finance University, Harbin, 150030, China

Abstract: The increasing reliance on credit risk assessment models within legal frameworks necessitates a balance between data utility and privacy preservation. This paper introduces a privacy-preserving legal credit risk assessment framework driven by generative AI, specifically leveraging a privacy-preserving generative adversarial network (ppGAN). The proposed framework aims to generate synthetic financial and legal data that preserves privacy while maintaining high utility for downstream legal credit risk models. The core idea of our approach is to leverage generative adversarial networks (GANs) to synthesise data that mimics real-world patterns without compromising sensitive information. Furthermore, we analyse the interpretability of these models, enabling stakeholders to understand the decision-making process behind risk assessments. The ppGAN architecture is evaluated against several baseline methods, including tGAN, MedGAN, HealthGAN, and DP-GAN, in terms of privacy protection, utility (classification accuracy, F1-score), and interpretability. Experimental results demonstrate that ppGAN achieves superior privacy protection and delivers high-performance credit risk predictions.

Keywords: privacy-preserving; generative AI; legal credit risk assessment; generative adversarial networks; GAN; interpretability analysis.

DOI: 10.1504/IJBIDM.2026.155248

International Journal of Business Intelligence and Data Mining, 2026 Vol.28 No.4/5/6, pp.331 - 347

Received: 21 Sep 2025
Accepted: 06 Jan 2026
Published online: 29 Jul 2026 *

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