Multi-Objective Optimization for Credit Card Fraud Classification: Achieving a Balance between Accuracy and Carbon Footprint within a Green AI Framework
As financial fraud becomes increasingly sophisticated, the demand for complex machine learning models has surged, inadvertently leading to a significant increase in computational energy consumption. This study addresses the critical trade-off between predictive accuracy and environmental sustainability within a Green AI framework. We propose a multi-objective optimization approach to evaluate Logistic Regression, Random Forest, and XGBoost on a highly imbalanced credit card fraud dataset. Experimental results reveal a non-linear “Carbon Cost of Complexity,” where the transition from linear to tree-based architectures yields diminishing returns; a 3.4% improvement in detection accuracy requires a 163% increase in training carbon emissions. The Random Forest model $(\mathbf{n}=\mathbf{2 0 0}, \mathbf{d}=\mathbf{1 5})$ emerged as the Pareto Optimal solution, achieving a superior F1-Score of 0.7512 and an AUPRC of 0.8031. Although XGBoost proved to be 62% more energy-efficient during the training phase, Random Forest demonstrated a distinct advantage in inference latency, achieving a throughput of 664,576 Transactions Per Second (TPS). We conclude that while Random Forest incurs a higher carbon footprint $\left(\mathbf{7. 1 5} \times \mathbf{1 0}^{-\mathbf{6}} \mathbf{~ k g C O} \mathbf{2 e q}\right.$ per cycle), this expenditure is justified by its robustness in preventing financial loss and its capability for real-time processing in high-stakes environments.