标题： 基于深度学习的金融欺诈检测的逐年发展：一项系统性文献回顾 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，这是金融领域的一个关键问题。采用Kitchenham系统的文献综述方法，分析了2019年至2024年间发表的57项研究。该综述强调了卷积神经网络、长短时记忆模型和变换器等不同深度学习模型在信用卡交易、保险索赔和财务报表审计等领域的有效性。评估了诸如精确度、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、特征工程和数据预处理的进步。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，以及自动化和保护隐私的技术机会，如区块链集成和主成分分析。通过考察过去五年的趋势，本综述确定了推进金融欺诈检测中深度学习应用的关键空白和有前景的方向，为研究人员和从业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.