Detecting fraudulent transactions in financial systems requires models that balance accuracy with interpretability. Although black-box machine learning models offer high predictive performance, their opacity limits trust and practical deployment in high-stakes domains like fraud detection. In this work, we present a Genetic Programming (GP) framework that evolves interpretable Boolean rule-based classifiers tailored for fraud detection. Our approach leverages both mutation and crossover operations to explore the space of candidate rules, enabling global optimization, and addresses the limitations of local search strategies. To ensure interpretability, we transform numerical features using a decision tree-based binarization technique that extracts class-aware binary thresholds. This allows the GP to evolve rules composed entirely of logical operators over binarized features, creating clear and interpretable models. Evaluated on the PaySim dataset, our approach exceeds the performance of other interpretable methods and remains competitive when compared with the state-of-the-art black-box models. Our results show that GP-evolved Boolean rules can serve as interpretable alternatives for real-world fraud detection systems.