Revolutionizing Business Credit Systems with Big Data: A Review of Modeling Techniques and Applications

Abstract

The rapid evolution of big data technology has significantly transformed the commercial credit evaluation system ecosystem. Traditional credit scoring systems, which rely on static financial data and centralized databases, are unable to capture the complex, real-time dynamics of modern borrowers. The paper provides a comprehensive review of how big data is revolutionizing commercial credit evaluation by highlighting credit modeling approaches, data sources, and regulatory frameworks. We classify big data applications across three dimensions: business models (e.g., B2C, P2P), credit subjects (enterprises vs. individuals), and financial contexts (e.g., agricultural or cross-border finance). Key machine learning models, e.g., Random Forest, XGBoost, and deep learning architecture —are reviewed in the context of their applicability to structured and unstructured data. In addition, the survey covers emerging data sources like IoT streams, mobile activity, and social media footprints, and preprocessing techniques like feature engineering, normalization, and dimensionality reduction. Ethical considerations such as algorithmic bias, transparency, and data protection regulations (e.g., GDPR) are addressed, along with newer trends including explainable AI, large language models, and federated learning. Drawing on more than 30 academic references, this report aims to support responsible and data-driven credit scoring processes in commercial financial systems.