Fourier-Based Adaptive Spectral Synthesis: Decision-Making with Imbalanced Management Data

Artificial intelligence applications in management, such as fraud detection and churn prediction, are frequently constrained by the class imbalance problem. Standard over-sampling methods, such as SMOTE, rely on local geometric interpolation, which assumes data convexity and struggles to model the disjoint structures typical of managerial datasets. We introduce Fourier-based Adaptive Spectral Synthesis (FASS), an over-sampling method that frames data generation as a signal reconstruction problem. By transforming the minority class data into the frequency domain via the empirical characteristic function, FASS isolates the global manifold structure from high-frequency sampling noise through an automated spectral filtering mechanism. We prove the L2-consistency of the underlying estimator. Empirical evaluations on credit and marketing datasets demonstrate that FASS favorably shifts the precision–recall trade-off compared to geometric baselines, reducing false positives and providing a theoretically consistent, parameter-free approach to learning from imbalanced data.