<![CDATA[The contemporary Peer-to-Peer (P2P) lending industry is currently facing existential sustainability challenges, primarily driven by intense market saturation and the escalating risk of Non-Performing Loans (NPL). Conventional business models, which rely heavily on interest-based income, are proving increasingly fragile in this volatile financial landscape. Addressing these critical vulnerabilities, this study proposes a comprehensive Enterprise Architecture (EA) framework to transform Fintech platforms from traditional loan intermediaries into intelligent, data-driven financial orchestrators effectively integrated within a Smart Urban Ecosystem. Methodologically, the research adopts a systematic qualitative Design Science Research (DSR) approach, synthesizing the Business Model Canvas for strategic alignment and ArchiMate 3.1 standards for technical structural modeling. The study critiques the limitations of existing monolithic systems and introduces a novel multi-stream revenue model designed to diversify income sources. This model comprises three core pillars: AI-based Credit Scoring as a Service (CSaaS) to foster cross-platform interoperability and trust, Credit Behavior Analytics, which monetizes previously underutilized "dark data" for deeper risk insights, and Embedded Credit Scoring tailored for real-time, high-frequency transactional environments. Technically, the proposed architecture strategically decouples high-load AI computation from low-latency decisioning processes using a microservices approach, thereby resolving scalability bottlenecks often found in legacy systems. The findings demonstrate that transitioning to this modular "Fintech-as-a-Service" paradigm significantly mitigates financial risk by shifting reliance from uncertain loan interest to stable, fee-based revenue streams. This research provides a strategic blueprint for Fintech stakeholders, offering a pathway to long-term viability and competitive advantage in the emerging data-driven digital economy.]]>
