This study proposes a novel distributionally robust portfolio optimization framework based on Wasserstein generative modeling, aiming to address the challenges of distributional uncertainty, tail risk, and structural drift in financial markets. The model integrates Wasserstein distance-based robust optimization with generative adversarial learning to jointly enhance risk control and return stability. Specifically, a Wasserstein generative adversarial network is employed to reconstruct the latent distribution of asset returns, enabling the capture of non-Gaussian features and tail dependencies in complex market environments. By constructing an uncertainty set under the Wasserstein metric, the optimization process achieves dynamic balance between empirical risk minimization and robustness to distributional perturbations. Furthermore, the framework incorporates a dual optimization mechanism that alternately updates generative and optimization parameters to adaptively align with changing market structures. Experimental evaluations on multi-asset datasets demonstrate that the proposed model achieves higher Sharpe ratios, lower maximum drawdowns, and improved robustness compared with conventional reinforcement learning-based and mean-variance methods. The results verify that integrating Wasserstein generative modeling into distributionally robust optimization provides an effective and interpretable pathway for achieving stable asset allocation and risk-aware decision-making under volatile financial conditions.

This work targets open-world credit risk identification where few labeled samples and emerging fraud scenarios coexist. It proposes a unified detection framework that integrates meta learning and domain adaptation. The goal is to improve rapid adaptation and cross-scenario robustness in dynamic environments. The method uses a shared encoder for representation learning over multiple source transaction features. At the task level, it introduces a meta learning training paradigm. A support set-driven fast update mechanism is used to learn transferable initialization and efficient parameter adjustment. This enables the model to form an effective decision boundary quickly under limited target domain labels. To mitigate distribution shift caused by channel switching, temporal drift, and changes in business strategies, the framework adds domain alignment constraints at the representation level. It learns domain-invariant risk features by minimizing the discrepancy between source and target latent representations. This systematically presents stable ranges and behavioral patterns under different risk bias settings and scenario changes. The study provides a technical solution for credit fraud detection in open environments that supports unified modeling, fast adaptation, and cross-domain robustness.