用AI识别银行欺诈交易，提升在线金融安全
Application of Artificial Intelligence for Fraudulent Banking Operations Recognition
- 构建基于神经网络的机器学习模型，优化数据预处理与特征工程
- 逻辑回归模型AUC达0.946，集成学习方法最高达0.954
- 适合关注金融风控、反欺诈系统的开发者与研究人员
本研究探讨利用人工智能识别银行欺诈行为。近年来，受新冠疫情推动，大量银行业务转向线上平台，催生了众多慈善基金，为犯罪分子提供了新的欺骗手段，导致银行欺诈事件频发。本文聚焦机器学习算法，用于分析和识别在线银行交易中的异常行为。研究创新在于构建了针对欺诈交易的机器学习模型，并提出数据预处理技术以支持模型比较与优选。通过处理高度不平衡的数据集、特征变换与特征工程等方法，显著提升检测精度。所提模型基于人工神经网络，有效改进了欺诈识别准确率。不同算法结果经可视化对比，逻辑回归模型表现最佳，AUC值约为0.946；堆叠泛化（stacked generalization）方法进一步提升至0.954。利用人工智能识别银行欺诈是数字社会中的重要课题。
原文摘要 · Abstract (English)
This study considers the task of applying artificial intelligence to recognize bank fraud. In recent years, due to the COVID19 pandemic, bank fraud has become even more common due to the massive transition of many operations to online platforms and the creation of many charitable funds that criminals can use to deceive users. The present work focuses on machine learning algorithms as a tool well suited for analyzing and recognizing online banking transactions. The study`s scientific novelty is the development of machine learning models for identifying fraudulent banking transactions and techniques for preprocessing bank data for further comparison and selection of the best results. This paper also details various methods for improving detection accuracy, i.e., handling highly imbalanced datasets, feature transformation, and feature engineering. The proposed model, which is based on an artificial neural network, effectively improves the accuracy of fraudulent transaction detection. The results of the different algorithms are visualized, and the logistic regression algorithm performs the best, with an output AUC value of approximately 0,946. The stacked generalization shows a better AUC of 0.954. The recognition of banking fraud using artificial intelligence algorithms is a topical issue in our digital society.
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