基于缴费行为轨迹追踪的渠道风险监控模型  

Model of Channel Risk Monitor Based on Tracking Payment Behaviors

作　　者：李捞扒 邹阳[1] 曾晓勤[1] LI Laoba;ZOU Yang;ZENG Xiaoqin(College of Computer and Information, Hohai University, Nanjing 211100)

机构地区：[1]河海大学计算机与信息学院,南京211100

出　　处：《微型电脑应用》2019年第4期65-70,共6页Microcomputer Applications

摘　　要：电费收取过程中的第三方缴费渠道隐藏着巨大的风险监控漏洞,对电网企业构成严重的资金安全隐患。在已明确风险类型、成因机理和表现形式的基础之上,从追踪客户的缴费行为轨迹出发,提出了一个针对缴费渠道的风险监控模型。先分析客户缴费行为记录并追踪其缴费行为轨迹,使用决策树算法与正则表达式相结合的方法判断出客户潜在的风险类型;然后确定各风险类型的评估指标,建立递阶的评估指标体系;最后使用加权最小二乘法创建风险评估模型,计算渠道各风险类型的风险指数,进而得到渠道的各风险排名。该模型将定性识别与定量评估分离,可以有效地监管渠道风险且获得了可信度较高的评估结果。It is found that there are still huge hidden risks in the third-party payment channels during the process of electricity charge collection, which poses serious capital security risks to power grid enterprises. Based on risk types, cause mechanisms and expression forms, a risk monitoring model of payment channels is proposed from the perspective on tracking the trajectory of customer's payment. The process is firstly to analyze the customer's payment records and track the trajectory of their payment behaviors, by combining the decision tree algorithm with regular expressions to judge potential risk types in the customer, then to confirm evaluation indicators and establish a hierarchical evaluation index system, and finally to establish a risk assessment model with the weighted least square method(WLS) to obtain the risk ranking of the channels from the risk index of each risk type. The model separates qualitative cognition from quantitative assessment, and can effectively monitor the channel's risk in the process of electricity charge collection and obtain higher reliability risk assessment results.

关 键 词：风险评估 轨迹追踪 决策树算法 回归分析 加权最小二乘法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]