Jul 2026· Journal of Fintech and Business Analysis· Vol 3, pp. 54-64· 0 citations
TL;DR
Experimental results reveal that the FinWGAN-GP model achieves high-fidelity generation and expansion for high-risk samples, and a balanced dataset is conducive to improving the training effect of the early warning model.
Abstract
Owing to the low occurrence frequencies of extreme financial risk events, risk prediction models are prone to being dominated by the majority of the normal samples encountered during the pretraining process when using financial risk data. This weakens the ability to effectively learn abnormal characteristics and thus reduces the sensitivity and stability of the developed predictive model. To overcome these limitations, a data class-balancing model, Financial Wasserstein Generative Adversarial Network with Gradient Penalty (FinWGAN-GP), is constructed on the basis of the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) framework. Experimental results reveal that the FinWGAN-GP model achieves high-fidelity generation and expansion for high-risk samples, and a balanced dataset is conducive to improving the training effect of the early warning model. Through the integration of multisource heterogeneous data and Artificial Intelligence (AI) methods, the proposed model can identify potential financial risks earlier and more accurately, helping enhance the forward-looking and scientific nature of financial regulation.
A multicenter learning method that leverages the advantage of stochastic neural networks (SNNs) for feature uncertainty learning and induces multiple centers for each class of samples in latent space to fit data more delicately, named the multicenter SNN (MC-SNN).
Meng Hu, Ran Wang, Yanting Guo et al.· IEEE Transactions on Cyberne...· 0 citations
Class imbalance in datasets remains a major problem in machine learning; most algorithms produce a biased model that generalizes poorly on the minority classes. This becomes an important factor in applications where minority Class samples are very important, such as medical diagnosis, accounting fraud detection, financial analysis, and so on. This paper proposes a new method to overcome the problem of class imbalance by using Generative Adversarial Networks (GANs). Specifically, the proposed method aims at obtaining a set of synthetic instances for the minority classes, which helps to balance the training set and improve classifiers’ performance. It introduces a modified Generative Adversarial Network (MGAN), which generates synthetic images of the minority class (128 × 128 × 3 pixels), thereby enhancing classifier resilience. Empirical evaluation indicates that MGAN surpasses conventional data balancing methods in medical imaging contexts. The effectiveness of this image filtering approach regarding lesion detection is experimentally confirmed when using two medical datasets of endoscopic and pathological images. The outcomes represent a high performance of the variant classifiers, which outperforms the conventional approaches when using the altered MGAN.
Roaa Razaq, Ebtesam N. Alshemmary, Zhentai Lu· Iraqi Journal of Science· 0 citations
Financial fraud detection is crucial in the banking and financial sectors, but it faces considerable challenges because of class imbalance in transaction data and the growing threat of adversarial attacks. These issues frequently undermine the effectiveness of deep neural networks despite their demonstrated potential in this domain. To address these challenges, this paper proposes a novel approach, adversarial training–based deep imbalanced learning (ATDIL), which integrates imbalanced learning and adversarial defense into a unified approach. ATDIL leverages an adversarial autoencoder to efficiently synthesize high-quality minority-class samples that are informative and adversarial, while maintaining low computational complexity. The effectiveness of ATDIL is rigorously validated through both theoretical and experimental analyses. Theoretically, the optimal solution form for the inner optimization problem in ATDIL is derived, and its convergence under mild assumptions is established. Extensive evaluations on seven real-world financial data sets demonstrate that ATDIL outperforms state-of-the-art imbalanced learning methods across multiple metrics while exhibiting superior resilience under adversarial conditions. This combination of theoretical guarantees and empirical evidence highlights ATDIL’s ability to effectively address class imbalance and model security, offering a robust and practical framework for enhancing financial fraud detection systems.
History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning.
Funding: This work was supported by the National Natural Science Foundation of China [Grant 72401208], the Key Program of the National Natural Science Foundation of China [Grant 72331007], the Natural Science Foundation of Sichuan Province [Grant 2025NSFSC1981], the Postdoctoral Fellowship Program of the China Postdoctoral Science Foundation [Grant GZB20240504], the International Visiting Program for Excellent Young Scholars of Sichuan University (SCU), and the Humanities and Social Science Youth Foundation of the Ministry of Education of China [Grant 23YJCZH088].
Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2025.1251 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2025.1251 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .
Unknown authors· INFORMS journal on computing· 0 citations
Single domain generalization (SDG) aims to learn a model from one labeled source domain that generalizes to unseen target domains. A common strategy is to enrich the source distribution with augmented or generated samples, and recent text-to-image (T2I) diffusion models provide a strong generative prior for this purpose. However, diversity alone is insufficient for robust generalization, because useful generated samples should also capture variations that the current classifier finds difficult. Motivated by distributionally robust optimization (DRO), we define a semantic ambiguity set in the class-conditional generative space of a pretrained T2I model and search it for samples with high classification loss under the current classifier. To this end, we introduce PAPT++, a risk-aware adversarial generation-training framework for SDG. PAPT++ first learns diverse semantic reference images for each class through image-text alignment and intra-class diversity regularization. These references then serve as denoising targets during classifier-guided diffusion synthesis, reducing semantic drift while guiding generation toward challenging variations. The generated samples are combined with the source data to update the classifier, and the updated classifier guides the next synthesis round in return. In this way, PAPT++ progressively exposes the classifier to challenging yet semantically consistent variations. Extensive experiments on standard SDG benchmarks demonstrate the superiority of the proposed PAPT++ method and the effectiveness of its main components.
Zhipeng Xu, De Cheng, Xinyang Jiang et al.· 0 citations
Predicting the risk of Alzheimer's disease (AD) is fundamental for early-stage intervention. Nevertheless, most methods struggle to extract multi-omics associative patterns due to the limited feature perception and inflexible disease modeling. This paper proposes a novel evolutionary pattern mining framework for precise disease risk prediction. Firstly, large foundational models are employed to automatically construct high-quality features. Second, a perceptual deformable attention mathematical model is proposed, which combines multi-scale sparse attention and deformable attention mechanisms to capture evolutionary patterns of fused multi-omics features. Finally, a Perceptual Deformable Attention Generative Adversarial Network (PDAT-GAN) is developed. PDAT-GAN can precisely simulate the evolutionary procedure of AD using multi-omics data, thereby achieving robust risk prediction and pathogeny extraction for AD. We validate the advanced performance and interpretability of PDAT-GAN on public datasets, underscoring significance of PDAT-GAN in supporting clinical intervention and pathogenetic research. The code of PDAT-GAN can be accessed at: .
Zhao-Xu Xing, Zheng Liu, Da-Fang Zhang et al.· Medical Image Anal.· 0 citations
Alzheimer's disease (AD) risk prediction relies on accurately characterizing pathological mechanisms underlying AD progression. However, existing methods struggle with heterogeneous multi-omics data and often fail to capture the spatiotemporal dynamics of the disease, limiting their predictive performance. In this paper, an integrated framework fusing spatial and temporal information is proposed to improve prediction capability. First, brain region-gene directed networks are constructed based on large foundation model-enhanced features. Second, a context perception attention model is designed to characterize topological changes of directed networks during AD progression. Based on this model, we develop a Context Perception Attention Generative Adversarial Network (CPA-GAN) that leverages adversarial training to mine AD evolutionary patterns, thereby supporting risk prediction and pathogeny extraction. Finally, the superiority, effectiveness, and robustness of CPA-GAN are validated by extensive experiments. Overall, this work provides a robust and effective modeling framework tailored for early-stage AD risk prediction.
Zhao-Xu Xing, Da-Fang Zhang, Kun Xie et al.· IEEE Transactions on Medical...· 0 citations
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