人工智能在银行欺诈操作识别中的应用
Application of Artificial Intelligence for Fraudulent Banking Operations Recognition
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中文总结 AI 辅助
该研究针对疫情下银行欺诈增多的问题，开发机器学习模型与数据预处理技术，通过处理不平衡数据、特征工程等方法，经实验发现堆叠集成算法AUC达0.954，可有效提升银行欺诈识别准确率。
中文摘要 AI 辅助
本研究探讨将人工智能应用于银行欺诈识别的任务。近年来，受COVID-19疫情影响，由于大量业务向线上平台转移，以及犯罪分子可利用众多慈善基金欺骗用户，银行欺诈行为愈发普遍。本研究聚焦于机器学习算法，将其作为分析和识别线上银行交易的合适工具。本研究的科学创新点在于开发用于识别欺诈性银行交易的机器学习模型，以及用于银行数据预处理的技术，以便进一步比较并选择最佳结果。本文还详细介绍了多种提升检测准确率的方法，即处理高度不平衡的数据集、特征转换和特征工程。所提出的基于人工神经网络的模型可有效提升欺诈交易检测的准确率。不同算法的结果被可视化呈现，其中逻辑回归算法表现最佳，输出的AUC值约为0.946；堆叠集成（stacked generalization）的AUC值更优，达0.954。利用人工智能算法识别银行欺诈是我们数字社会中的一个热点问题。
英文摘要
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.
