Darwish Saad M, Salama Amr Ibrahim, Elzoghabi Adel A
Department of Information Technology, Institute of Graduate Studies and Research, Alexandria University, 163 Horreya Avenue, El Shatby, P.O. Box 832, Alexandria, 21526, Egypt.
College of Computers and Data Science, Alexandria University, Postal code 21568, Alexandria, Egypt.
Sci Rep. 2025 May 23;15(1):17983. doi: 10.1038/s41598-025-01223-8.
Detecting online fraudulent trading in the realm of Fintech presents several challenges, primarily due to the dynamic nature of financial markets and the evolving tactics of fraudsters. Traditional machine learning algorithms trained on unbalanced datasets tend to bias towards the majority class (legitimate transactions) and may overlook minority class (fraudulent transactions) patterns. This bias can lead to poor performance in detecting fraudulent activities. The choice of sampling technique (e.g., oversampling, undersampling, SMOTE) can significantly impact model performance. However, selecting the appropriate sampling strategy requires domain knowledge and experimentation, which can be time-consuming and resource-intensive. This work utilizes Artificial Bee Colony (ABC)-based sampling to tackle class imbalance in credit card fraud detection. By generating realistic synthetic fraud samples, ABC-sampling helps the model learn fraudulent patterns more effectively without favoring non-fraudulent transactions. Inspired by the foraging behavior of bees, the process involves exploring existing fraud patterns, selecting the most relevant ones, creating synthetic fraud samples, and refining them to ensure they closely resemble real fraud cases while preserving key features that distinguish fraud from regular transactions. This method enhances fraud detection accuracy and minimizes false alarms, making the system more reliable. The suggested model employs anomaly detection algorithm to identify unusual or fraudulent trading activities in which it creates behavioral profiles for individual traders based on their historical trading activities and utilizes machine learning algorithm to cluster traders into groups based on similar behavior patterns. Then it identifies characteristic features of fraudulent traders such as sudden changes in trading volume, irregular trading hours, or trading activities inconsistent with their profile. By analyzing patterns and anomalies in traders' behavior, these approaches can effectively identify suspicious activities indicative of fraudulent behavior. Extensive performance studies demonstrate that the proposed algorithm significantly outperforms the state-of-the-art methods by 10% in terms of accuracy.
在金融科技领域检测在线欺诈交易存在诸多挑战，主要是由于金融市场的动态性质以及欺诈者不断演变的策略。在不平衡数据集上训练的传统机器学习算法往往偏向多数类（合法交易），可能会忽略少数类（欺诈交易）模式。这种偏差可能导致在检测欺诈活动时性能不佳。采样技术（例如，过采样、欠采样、SMOTE）的选择会对模型性能产生重大影响。然而，选择合适的采样策略需要领域知识和实验，这可能既耗时又耗费资源。这项工作利用基于人工蜂群（ABC）的采样来解决信用卡欺诈检测中的类不平衡问题。通过生成逼真的合成欺诈样本，ABC采样有助于模型更有效地学习欺诈模式，而不会偏向非欺诈交易。受蜜蜂觅食行为的启发，该过程包括探索现有的欺诈模式、选择最相关的模式、创建合成欺诈样本并对其进行优化，以确保它们与真实欺诈案例非常相似，同时保留区分欺诈与正常交易的关键特征。这种方法提高了欺诈检测的准确性并最大限度地减少误报，使系统更加可靠。所建议的模型采用异常检测算法来识别异常或欺诈性交易活动，其中它根据个体交易者的历史交易活动创建行为档案，并利用机器学习算法根据相似的行为模式将交易者聚类成组。然后它识别欺诈交易者的特征，例如交易量的突然变化、不规则的交易时间或与其档案不一致的交易活动。通过分析交易者行为中的模式和异常，这些方法可以有效地识别表明欺诈行为的可疑活动。广泛的性能研究表明，所提出的算法在准确性方面比现有最先进的方法显著高出10%。
