计算机科学 ›› 2023, Vol. 50 ›› Issue (2): 300-309.doi: 10.11896/jsjkx.220800169
李炜卓1,3,4, 卢冰洁2, 杨骏铭1, 那崇宁2
LI Weizhuo1,3,4, LU Bingjie2, YANG Junming1, NA Chongning2
摘要： 车险欺诈检测对促进汽车保险业的良性健康发展有着重要意义。由于欺诈的判断涉及公民权利等核心内容,需要车险专家对案件进行核查,提供欺诈原因。尽管基于机器学习的方法泛化能力强、精确度高,但缺少可解释性,而基于专家系统的规则方法尽管有较好的可解释性,但受限于规则复杂的触发条件。为了解决未触发专家系统欺诈规则而被机器学习方法检测为“欺诈”的案件无法被解释的问题,文中提出了基于网络表示学习的车险欺诈溯因分析方法。该方法首先定义了车险欺诈溯因分析任务,然后采用网络表示学习对已触发专家系统中欺诈规则的案件进行案件-规则因子网络的建模,学习欺诈规则中因子的分布式向量表示。为了更好地度量“欺诈”案件与专家系统中因子未全部触发规则之间的相似度,该方法基于溯因缺省原理,设计了一种规则因子的加权拼接策略来缓解训练数据不足的问题。实验结果表明,所提方法相较于已有方法在车险欺诈溯因预测任务的3项指标中均能取得更好的效果。
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