mlsanityData to 7 Oct 2026

Wasserstein Distance-Weighted Adversarial Network for Cross-Domain Credit Risk Assessment

arXiv 2409.18544 ↗First posted 27 Sep 2024cs.LGcited by 11

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Abstract (arXiv)

This paper delves into the application of adversarial domain adaptation (ADA) for enhancing credit risk assessment in financial institutions. It addresses two critical challenges: the cold start problem, where historical lending data is scarce, and the data imbalance issue, where high-risk transactions are underrepresented. The paper introduces an improved ADA framework, the Wasserstein Distance Weighted Adversarial Domain Adaptation Network (WD-WADA), which leverages the Wasserstein distance to align source and target domains effectively. The proposed method includes an innovative weighted strategy to tackle data imbalance, adjusting for both the class distribution and the difficulty level of predictions. The paper demonstrates that WD-WADA not only mitigates the cold start problem but also provides a more accurate measure of domain differences, leading to improved cross-domain credit risk assessment. Extensive experiments on real-world credit datasets validate the model's effectiveness, showcasing superior performance in cross-domain learning, classification accuracy, and model stability compared to traditional methods.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 11 of its 18 references that are in the map. First posted on arXiv 27 Sep 2024.

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11 of 18 references.

References · 11

  1. 2017Towards Principled Methods for Training Generative Adversarial Networkscited by 460
  2. 2024Survival Prediction Across Diverse Cancer Types Using Neural Networkscited by 80
  3. 2024Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an examplecited by 50
  4. 2024Dynamic Fraud Detection: Integrating Reinforcement Learning into Graph Neural Networkscited by 19
  5. 2024A comparative study of generative adversarial networks for image recognition algorithms based on deep learning and traditional methodscited by 12
  6. 2024Enhancing Medical Imaging with GANs Synthesizing Realistic Images from Limited Datacited by 11
  7. 2024Exploration of Multi-Scale Image Fusion Systems in Intelligent Medical Image Analysiscited by 10
  8. 2024Algorithm Research of ELMo Word Embedding and Deep Learning Multimodal Transformer in Image Descriptioncited by 10
  9. 2024Applying Conditional Generative Adversarial Networks for Imaging Diagnosiscited by 9
  10. 2024Enhancing Convolutional Neural Networks with Higher-Order Numerical Difference Methodscited by 8
  11. 2024Enhancing Deep Learning with Optimized Gradient Descent: Bridging Numerical Methods and Neural Network Trainingcited by 7

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Citing papers per year: 2024: 11.

11 papers.

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  4. 2024Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classificationcited by 11
  5. 2024Deep Learning for Cross-Border Transaction Anomaly Detection in Anti-Money Laundering Systemscited by 9
  6. 2024Robust Graph Neural Networks for Stability Analysis in Dynamic Networkscited by 8
  7. 2024Automated Genre-Aware Article Scoring and Feedback Using Large Language Modelscited by 7
  8. 2024Deep Learning with HM-VGG: AI Strategies for Multi-modal Image Analysiscited by 7
  9. 2024Reinforcement Learning for Adaptive Resource Scheduling in Complex System Environmentscited by 7
  10. 2024Optimizing Retrieval-Augmented Generation with Elasticsearch for Enhanced Question-Answering Systemscited by 5
  11. 2024Leveraging Generative Adversarial Networks for Addressing Data Imbalance in Financial Market Supervisioncited by 5