Explainable AI Frameworks for Regulatory-Compliant Buy-Now-Pay-Later Credit Risk Assessment in Real-Time Cloud Banking Architectures
DOI:
https://doi.org/10.22399/ijcesen.5107Keywords:
XAI, BNPL, GDPR, Real Time, Banking ArchitectureAbstract
The rapid proliferation of Buy-Now-Pay-Later (BNPL) services has transformed digital lending ecosystems, necessitating robust, scalable, and transparent credit risk assessment frameworks. Traditional credit scoring mechanisms are insufficient for BNPL contexts characterized by real-time decisioning, thin credit files, and dynamic consumer behavior. This paper presents a comprehensive academic analysis of Explainable Artificial Intelligence (XAI) frameworks tailored for regulatory-compliant BNPL credit risk assessment within real-time cloud banking architectures. The study synthesizes advances in machine learning, interpretability techniques, regulatory mandates (e.g., GDPR), and cloud-native financial infrastructures. The paper proposes a layered architectural framework integrating explainability, fairness, and compliance into AI-driven credit decision systems. The discussion aligns technological developments with evolving financial regulations and highlights open research challenges.
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