Bias-Resistant Credit Scoring Using Blockchain Data and Explainable ML

Abstract

Bias in credit scoring remains a persistent challenge due to opaque data sources, centralized modeling pipelines, and limited transparency in model decisions. This paper proposes a bias-resistant credit-scoring framework that leverages blockchain-recorded financial activity and explainable machine learning to enhance fairness, interpretability, and auditability. The system integrates scalable sharded-ledger designs to support high-throughput, tamper-evident feature provenance, enabling lenders to rely on verifiable behavioral signals rather than demographic proxies. A decentralized federated learning mechanism ensures that model training occurs across distributed financial institutions without exposing raw user data, reducing privacy risks and systemic bias. To further strengthen resilience, the architecture incorporates diversity-enhancing consensus mechanisms and geographic risk-aware preprocessing to mitigate structural distortions frequently observed in transaction monitoring. Explainable ML techniques—including feature attribution, counterfactual reasoning, and rule-based local explanations—enable transparent assessment of creditworthiness and support regulatory audit requirements. Experiments on synthetic and real-world consortium datasets demonstrate that the proposed approach achieves competitive predictive accuracy, significantly lowers disparate impact across protected groups, and improves traceability of model decisions. The findings indicate that combining blockchain-secured data provenance with interpretable learning architectures offers a practical pathway toward ethical, compliant, and globally deployable credit-scoring systems.