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一个基于数值的机器学习设计,用于检测数字市场上有组织的零售欺诈行为

Abed Mutemi¹, Fernando Bacao²

¹NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312, Lisboa, Portugal. d20200455@novaims.unl.pt.

Scientific reports

|August 2, 2023

概括

有组织的零售犯罪 (ORC) 是一个不断增长的威胁. 本研究引入了一种机器学习方法,用于检测市场上的ORC列表,从而在识别欺诈活动时获得高准确性.

科学领域:

计算机科学计算机科学
犯罪学犯罪学
数据科学数据科学数据科学

背景情况:

有组织的零售犯罪 (ORC) 对零售商,在线市场和消费者构成重大威胁,由于电子商务的扩张,其流行率越来越高.
现有的欺诈检测研究主要集中在金融服务上,在专门针对ORC的研究中留下了一个空白.
ORC的财务和安全影响很大,预计随着互联网连接的增加,将会升级.

研究的目的:

开发和展示一个可扩展的机器学习策略,用于检测和隔离电子商务平台上的有组织零售犯罪 (ORC) 列表.
通过提供一种新的方法来解决ORC检测研究的短缺问题.
建立一个能够区分欺诈性列表和合法列表的系统.

主要方法:

采用监督学习方法,利用历史的买家和卖家行为和交易数据.
该框架包括定制数据预处理,特征选择 (58个特征中的45个),以及先进的类不平衡解决技术.
优化了分类算法,以有效地区分欺诈和合法市场上市.

主要成果:

性能最好的检测模型在持久数据集上获得了0.97的回忆得分.
该模型表现出强大的概括能力,在非样本测试数据上回忆得分为0.94.
该方法成功地使用一套精细的45个特征识别了ORC列表.

结论:

拟议的机器学习战略提供了一个可扩展和有效的解决方案,用于在线市场上检测有组织零售犯罪 (ORC).
该研究成功地为ORC检测的有限知识体系做出了贡献,提供了一个实际的框架.
高回忆分数表明该模型有潜力显著减轻ORC对企业和消费者的影响.

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