Macro-Profile Clustering and Banking Vulnerability in a Commodity-Dependent Monetary Union: Evidence from CEMAC

Early-warning systems for banking crises typically pool all member states into a single panel, assuming that macroeconomic shocks affect every economy in the same way ̶ an assumption that is convenient but rarely true. We argue that this assumption introduces a systematic and quantifiable predictive bias, and propose unsupervised macro-profile clustering as a tractable corrective. Using the Central African Economic and Monetary Community (CEMAC) ̶ a six-member commodity-dependent monetary union ̶ as a laboratory, we apply unsupervised clustering methods to 144 country-year observations and eleven macroeconomic features, recovering four macro-financial profiles: low-growth diversified, volatile oil-boom, fiscally strong high-revenue, and debt-distress. All six member states switch profile simultaneously at a single point in time, providing clustering-based evidence of a structural macro-financial regime change in CEMAC. Augmenting a pooled logistic early-warning benchmark with cluster membership indicators increases the five-fold cross-validated AUC-ROC from 0.588 to 0.642 ̶ a gain of 5.5 percentage points that translates into a 62 percent improvement in discriminatory power. The methodology requires only publicly available macroeconomic data and standard clustering software, making it directly replicable in other emerging-market monetary unions including WAEMU, the Gulf Cooperation Council, and the East African Community ̶ where structural divergence similarly undermines the effectiveness of uniform prudential frameworks. Our findings demonstrate that structural heterogeneity is a first-order consideration in the design of macroprudential early-warning systems for commodity-dependent emerging econo