Thai SME Credit Risk Assessment via Spectral Operator Algebra: From Machine Learning Classification to Direct Structure Measurement

We present a spectral operator algebra approach to Thai SME credit risk assessment that achieves F-measure of 0.8764 and AUC of 0.9690 using \textbf{zero training data}---a 17.2\% improvement over the best machine learning result (AdaBoost + SMOTE, F-measure 0.7478) reported by Wongwiwat et al. (2025). While ML approaches require labeled samples, class balancing techniques, and hyperparameter tuning, our method directly reads credit risk structure from the same 13 financial variables through four spectral operators: CASHFLOW+, GROWTH+, LEVERAGE+, and STABILITY+. The fundamental insight is that credit risk is not a classification problem requiring statistical inference---it is observable financial structure accessible through proper mathematical operators. Profinite continuity guarantees exclude deviant classification rules, ensuring alignment between measurement and reality.