This paper introduces a hybrid credit assessment methodology tailored specifically to the unique financial, informational, and operational challenges faced by importers. Importers operate at the intersection of cross-border financial risk, fluctuating supply chains, and opaque transactional histories - factors that challenge conventional credit models. While banks rely on structured financial records and established credit histories, P2P platforms harness alternative data and decentralized trust mechanisms such a peer feedback and real-time behavioral signals. Rather than merely merging technologies, the proposed approach integrates differing capabilities: institutional data interpretation dynamical risk modeling, and platform-based investor participation. This synthesis enables a more adaptive, inclusive, and context-aware assessment process. Core components include continuous data validation, alternative trade-data integration, machine learning-driven decision support, and modular feedback loops from lender communities. The methodology is evaluated through a structured literature review and illustrative case applications. By focusing on the informational volatility and operational complexity unique to import-driven businesses, this research contributes a targeted solution to a global financing gap - advancing both credit innovation and trade accessibility.