Since the existing machine-learning-based approaches for fraud detection are incapable of providing explanations, we propose a fraud detection method based on quantitative argumentation, which is intrinsically interpretable. First, we construct an argumentative tree by combining human-level knowledge and the knowledge learned from data. Second, we extend the existing quantitative argumentation debates (QuAD) frameworks by adding correlation strength between arguments and exploit the particle swarm optimization algorithm (PSO) to identify the correlation strength between arguments. Third, the performance of the new method is investigated by an empirical study, using the data from Ant Financial, the Alibaba Group's financial services provider. The results show that the new method has better performance than the existing DF-QuAD algorithm and is competitive with other machine learning methods, including Xgboost, ANN, SVM, and LR.

中文翻译：

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电子商务交易欺诈检测的优化定量论证辩论模型


由于现有的基于机器学习的欺诈检测方法无法提供解释，因此我们提出了一种基于定量论证的欺诈检测方法，该方法本质上是可解释的。首先，我们结合人类水平的知识和从数据中学到的知识来构建论证树。其次，我们通过增加参数之间的相关强度来扩展现有的定量论证辩论（QuAD）框架，并利用粒子群优化算法（PSO）来识别参数之间的相关强度。第三，利用阿里巴巴集团旗下金融服务提供商蚂蚁金服的数据，对新方法的性能进行了实证研究。结果表明，新方法比现有的 DF-QuAD 算法具有更好的性能，并且与其他机器学习方法（包括 Xgboost、ANN、SVM 和 LR）具有竞争力。