Empirical Validation of the 2Q-H-C-T-L-I Framework: A Hybrid Adaptive Non-Compensatory Credit Scoring Framework for Blockchain-Native Real World Asset (RWA) Lending

Abstract

Existing blockchain-native Real World Asset (RWA) lending protocols rely on binary pass/fail underwriting, single-metric financial ratios, or arithmetic aggregation — allowing strong performance on one dimension to compensate for material weakness on another. No existing protocol integrates cryptographic data integrity, legal enforceability, and identity binding as independently scored credit dimensions, nor addresses the unique risk profile of SME borrowers — particularly key-person concentration and succession failure. This paper addresses both gaps.

We present the 2Q–H–C–T–L–I framework: a three-tier, seven-component hybrid adaptive non-compensatory credit scoring model for institutional RWA invoice financing. All three tiers use adaptive geometric product aggregation: ; (Governance Layer); . The final composite score is where tier weights w₁, w₂, w₃ are adaptive. A score of 0 on any component collapses the tier score to 0 and S_rwa to 0 — the non-compensatory enforcement mechanism requires no separate hard gate. The complete adaptive parameter set (α, β, γ, δ, w₁, w₂, w₃, A₁, A₂, A₃) enables recalibration across jurisdictions, asset classes, borrower types, and lending contexts without structural redesign. For SME contexts where DAO validator infrastructure is unavailable, a six-component configuration (C omitted, T₂ = A₂ × H) is an explicit deployment option.

The framework is validated using a fully synthetic dataset constructed for research, education, and academic demonstration purposes — no real company, individual, or transaction is referenced. The synthetic case represents a Singapore invoice financing application (Tech-Fab Manufacturing Ltd., USD 250,000, Net 90) with 5-year financial statements, 24-month cash flow data, independent field verification, inspection, valuation, legal opinion, DAO governance vote, and full KYC/KYB/on-chain identity verification. Empirical results: T₁ = 0.8297, T₂ = 0.9965, T₃ = 0.9996, S_rwa = 0.9356 — Full Approval (w₁ = 0.35, w₂ = 0.30, w₃ = 0.35). Three stress tests confirm framework integrity across all three tiers. Binding constraints identified: Leverage (Lev = 0.5758) and Succession (score = 0.72) — the latter representing the most critical and commonly overlooked SME credit risk.

Four contributions: (C1) geometric mean non-compensatory aggregation across heterogeneous evidence types; (C2) oracle integrity as a continuously scored credit dimension; (C3) mandatory identity conjunction integrating KYC, KYB, and on-chain token binding; and (C4) a ten-parameter adaptive system (α, β, γ, δ, w₁, w₂, w₃, A₁, A₂, A₃) enabling institutional cross-context deployment with documented ±0.05 calibration guidance. Framework maps to ERC-3643 [1], ERC-4626 [2], Basel III [3], and MiCA [4]. Single-case validation (N = 1); portfolio backtesting and multi-assessor inter-rater study deferred to future work.

Keywords: JEL Classification: G21 (Banks, Depository Institutions, G23 (Non-bank Financial Institutions, Financial Instruments), G32 (Financing Policy, Capital and Ownership Structure), O33 (Technological Change: Choices and Consequences), RWA credit scoring, blockchain credit scoring, Adaptive Non-Compensatory Credit Scoring Framework for Blockchain, 2Q-H-C-T-L-I Framework, 2Q-H-C-T-L-I Model, RWA lending, RWA finance, Real World Asset tokenization, DeFi credit risk, non-compensatory scoring, adaptive weights, SME credit, succession risk, DAO governance, invoice financing, KYC/KYB, oracle reliability, synthetic dataset, Empirical Validation, Empirical Validation of the 2Q-H-C-T-L-I Framework

JEL Classification: G21, G23, G32, O33