Translating Platform Signals into Credit Features: A Framework from Korea’s E-commerce Ecosystem

Platform-generated behavioral data may improve credit assessment, but less is known about how those signals become usable regulated credit features. This conceptual review develops a four-stage translation framework and illustrates it with three Korean e-commerce initiatives: an anonymized bank–platform partnership observed by the author, Naver Financial’s SmartStore merchant program and an 11st × SK Telecom vignette. A later score, first deployed for personal credit, is included for context. Signal availability alone did not explain institutional use. Standardization, feature construction, validation and integration were directly observable constraints in Case A; public sources reveal less of that work in the other initiatives. Underwriting authority, data architecture and organizational commitment provide dimensions for comparing where features entered lending decisions. The cases differ in evidentiary depth and do not support causal comparisons. The paper treats the translation pipeline as its unit of analysis and considers implications for Korea’s 2026 Small Business Credit Bureau initiative.