Machine Learning Based Credit Evaluation and Risk Control in Digital Finance Platforms - David Laurent Mensah (Emerging Frontiers Library for The American Journal of Engineering and Technology, 2025)
The rapid transformation of global financial systems through artificial intelligence, big data analytics, and digital platforms has fundamentally altered the logic of credit allocation, risk assessment, and financial inclusion. Traditional credit scoring systems, historically reliant on static financial statements and limited borrower histories, are increasingly unable to accommodate the complexity, speed, and heterogeneity of modern digital economies. In response, algorithmic credit intelligence systems have emerged as a new paradigm in financial decision making, leveraging real time data streams, machine learning models, and automated risk engines to evaluate borrowers continuously and dynamically. This article develops a comprehensive theoretical and empirical synthesis of real time credit scoring and risk governance in digital lending platforms, grounding its analysis in contemporary financial technology research and regulatory discourse. Central to this analysis is the growing body of scholarship that demonstrates how real time data processing architectures combined with artificial intelligence enable adaptive and granular credit evaluation across diverse borrower segments, as shown in recent loan platform studies that emphasize continuous monitoring and predictive risk intelligence (Modadugu et al., 2025). Methodologically, the article adopts a qualitative, theory driven research design that integrates systematic literature synthesis, comparative institutional analysis, and interpretive modeling of digital credit infrastructures. Rather than presenting numerical estimations or algorithmic equations, the study focuses on the conceptual architecture of real time credit intelligence systems and their socio economic implications. The results demonstrate that digital lending platforms that integrate continuous data flows with adaptive risk analytics are capable of significantly improving portfolio performance, default prediction, and operational efficiency, while also generating new forms of systemic risk through model opacity, data concentration, and cyber vulnerability. These findings resonate with studies on machine learning in fintech and emerging market digital banking, which highlight both the efficiency gains and the governance dilemmas inherent in algorithmic finance (Gambacorta et al., 2024; Nnaomah et al., 2024). The discussion critically evaluates the regulatory and ethical dimensions of real time credit scoring, engaging with legal scholarship on responsible AI, digital discrimination, and financial governance. It argues that the future sustainability of algorithmic lending depends on the development of hybrid regulatory frameworks that combine technological oversight, institutional accountability, and participatory data governance. By integrating insights from cybersecurity, procurement analytics, and strategic data management, the article proposes a holistic model of algorithmic credit governance that aligns technological innovation with social trust and economic stability. Ultimately, this research contributes to the academic and policy debate by demonstrating that real time credit intelligence is not merely a technical upgrade but a profound institutional transformation that redefines how risk, trust, and opportunity are constructed in digital economies.