标题： 年度金融欺诈检测中深度学习的发展：系统文献综述 显示英文标题

标题： Year-over-Year Developments in Financial Fraud Detection via Deep Learning: A Systematic Literature Review

摘要： 本文系统回顾了深度学习(DL)技术在金融欺诈检测中的进展，这是金融行业的一个关键问题。 使用Kitchenham系统文献综述方法，分析了2019年至2024年间发表的57项研究。 该综述强调了各种深度学习模型的有效性，如卷积神经网络、长短期记忆网络和变压器，在信用卡交易、保险索赔和财务报表审计等领域的应用。 评估了精确率、召回率、F1分数和AUC-ROC等性能指标。 探讨的关键主题包括数据隐私框架的影响以及特征工程和数据预处理方面的进展。 该研究强调了不平衡数据集、模型可解释性和伦理考虑等挑战，同时提出了自动化和隐私保护技术的机会，如区块链集成和主成分分析。 通过审视过去五年的发展趋势，本综述识别了推动深度学习在金融欺诈检测中应用的关键差距和有前景的方向，为研究人员和实践者提供了可行的见解。 显示英文摘要

摘要： 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.