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

This paper presents the 2Q-H-C-T-L-I model: a three-tier, seven-component hybrid adaptive non-compensatory credit scoring framework designed for Real World Asset (RWA) tokenization and on-chain lending. The framework integrates seven structurally distinct intelligence components quantitative financial data (Q₁), qualitative structural assessment (Q₂), human expert judgment (H), DAO governance consensus (C), cryptographic data truth (T), legal enforceability (L), and ownership legitimacy (I)-into a unified institutional-grade risk scoring architecture. The mathematical engine employs geometric mean aggregation at every tier, enforcing strict noncompensatory behavior: a critical deficiency in any component cannot be masked by strength elsewhere. A mandatory hard-gate constraint at Tier 3 ensures that no credit score can be issued when the Trust (T), Legal Enforceability (L), or Ownership Legitimacy (I) components fall below defined minimum thresholds. The framework maps to ERC-3643, ERC-4626, Basel III, and MiCA compliance requirements, and is designed for institutional adoption in blockchain-native lending protocols. This paper provides the formal mathematical definition, system architecture, constraint logic, adaptive weighting system, and a one-line academic definition suitable for peer-reviewed publication.

Keywords: Real World Asset tokenization, DeFi credit risk, non-compensatory scoring, geometric mean aggregation, blockchain lending, RWA, DAO governance, KYC/KYB, Basel III, MiCA, ERC-3643, ERC-4626, Institutional DeFi, Credit Scoring, RWA Credit Scoring Model & Lending, DAO consensus, Trust Legal & Identity ensured RWA credit scoring and lending, Quantitative and qualitative judgement, DeFi Scoring Model

JEL Classification: G21, G32, O33