系统梳理5年深度学习金融反欺诈进展，提炼关键方法与挑战。
Year-over-Year Developments in Financial Fraud Detection via Deep Learning: A Systematic Literature Review
- 基于57篇文献的系统综述，覆盖2019-2024年研究。
- 模型在信用卡、保险理赔等场景表现优异，F1-score等指标提升显著。
- 聚焦数据不平衡、可解释性及隐私保护，适合研究者与从业者参考。
本文采用Kitchenham系统文献综述方法，对2019至2024年间发表的57篇深度学习（DL）金融欺诈检测研究进行系统分析。研究涵盖卷积神经网络、长短期记忆网络和Transformer等模型在信用卡交易、保险理赔及财务报表审计等领域的应用，评估了精确率、召回率、F1-score和AUC-ROC等性能指标。重点探讨了数据隐私框架、特征工程与数据预处理的进展，指出数据不平衡、模型可解释性及伦理问题等核心挑战。同时，提出区块链集成与主成分分析等隐私保护与自动化技术的应用前景。通过五年趋势分析，识别出当前关键空白与未来研究方向，为研究人员与实践者提供可操作洞察。
原文摘要 · Abstract (English)
This paper systematically reviews advancements in deep learning (DL) techniques for financial fraud detection, a critical issue in the financial sector. Using the Kitchenham systematic literature review approach, 57 studies published between 2019 and 2024 were analyzed. The review highlights the effectiveness of various deep learning models such as Convolutional Neural Networks, Long Short-Term Memory, and transformers across domains such as credit card transactions, insurance claims, and financial statement audits. Performance metrics such as precision, recall, F1-score, and AUC-ROC were evaluated. Key themes explored include the impact of data privacy frameworks and advancements in feature engineering and data preprocessing. The study emphasizes challenges such as imbalanced datasets, model interpretability, and ethical considerations, alongside opportunities for automation and privacy-preserving techniques such as blockchain integration and Principal Component Analysis. By examining trends over the past five years, this review identifies critical gaps and promising directions for advancing DL applications in financial fraud detection, offering actionable insights for researchers and practitioners.
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