A Novel Blockchain Architecture for Secure and Transparent Credit Regulation

Accurate and automated credit assessment systems are fundamental to the integrity of financial ecosystems, underpinning responsible lending, risk mitigation, and sustainable economic growth. In light of persistent economic uncertainties and an increasing frequency of credit defaults, financial entities face urgent demands for robust and scalable risk evaluation tools. While a diverse array of statistical and machine learning techniques have been proposed for credit scoring, prevailing methods remain labor-intensive and operationally cumbersome. This paper introduces VeriCred, a novel credit evaluation framework that synergistically combines automated machine learning with blockchain-based oversight to overcome these limitations. The proposed approach incorporates a data augmentation strategy to enrich limited and heterogeneous credit datasets, thereby improving model generalization. A distinctive blockchain layer is embedded to immutably trace data provenance and model decisions, ensuring full auditability. By orchestrating the end-to-end workflow—including feature extraction, hyperparameter optimization, and model selection—within a unified AutoML pipeline, the system drastically reduces manual dependency. Architecturally, the framework introduces C-NAS, a neural architecture search mechanism customized for credit risk prediction, alongside A-Triplet loss, an objective function tailored to refine feature discrimination. To address opacity concerns, an interpretability component elucidates feature contributions and model reasoning. Empirical evaluations demonstrate that VeriCred achieves superior predictive accuracy with significantly reduced computational overhead, offering financial institutions a transparent, efficient, and trustworthy credit scoring solution.