This paper introduces a novel, parsimoniously designed fuzzy logic architecture for Anti-Money Laundering (AML) transaction monitoring (TxM). In contrast to traditional Boolean rule-based systems, our approach employs a minimal yet sufficient fuzzy rule design framework that integrates essential components—flexible membership functions, semantic variable transformation, and statistical test results—to capture the inherent uncertainties in financial transactions. By leveraging a proprietary synthetic dataset that closely mirrors real banking behaviors, our model demonstrates significant improvements in recall, while mitigating vulnerabilities such as threshold manipulation. The parsimony of our design ensures that the architecture remains simple and interpretable, proving that a lean, carefully calibrated fuzzy logic system can effectively address key weaknesses in conventional AML monitoring approaches.