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Dynamic multi-level graph enhanced contrastive learning for financial fraud detection

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  • Lin, Wayne
  • Shi, Ya
  • Liu, Zhuang

Abstract

The escalating economic repercussions of financial fraud demand advanced detection methods. However, existing approaches are often limited by poor dynamic adaptability, challenges in few-shot learning scenarios, and heavy reliance on feature engineering. In this work, we present DyME-CLFD, a few-shot Dynamic Multi-level Graph Enhanced Contrastive Learning framework for Fraud Detection. DyME-CLFD efficiently captures intricate dependencies through dynamic aggregations, enhances discernment with multi-view contrastive learning, and incorporates spectral filtering for few-shot label integration. Extensive experiments demonstrate DyME-CLFD’s superiority in fraud detection across real-world datasets. Our findings present a significant contribution to the field and offer a robust framework for enhancing financial security.

Suggested Citation

  • Lin, Wayne & Shi, Ya & Liu, Zhuang, 2026. "Dynamic multi-level graph enhanced contrastive learning for financial fraud detection," Pacific-Basin Finance Journal, Elsevier, vol. 98(C).
    • Handle: RePEc:eee:pacfin:v:98:y:2026:i:c:s0927538x26001186
    DOI: 10.1016/j.pacfin.2026.103172
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