电子商务交易风险的概率统计模型与防范策略

Probability and Statistical Models of E-Commerce Transaction Risks and Prevention Strategies

摘要: 电子商务交易风险涉及支付、信用与物流多个环节，传统基于经验规则的管控手段在常态与高峰场景间难以兼顾灵敏度与稳健性。本模型主要针对非促销常态期的交易风险建模，对于“双十一”等高峰场景，提出了一套基于场景切换的扩展框架。以某电商平台2023年公开披露的100万笔交易为样本，构建了基于泊松分布、正态分布与Copula函数的联合概率风险模型，并提出动态预警与分级管控策略。描述性统计显示，支付风险月度频数均值为12，方差为12.8，接近泊松分布假设；信用损失偏度为0.35，峰度为3.1，基本符合正态分布特征。拟合优度检验进一步实证结果表明，泊松分布和正态分布可作为描述常态下支付风险频数与信用损失中心区间的有效近似。引入Copula进行依赖性诊断后，确定使用t-Copula刻画三类风险的对称尾部相依结构。策略实施后，支付风险强度由12次/月降至8次/月，信用平均单笔损失由500元降至350元，物流风险结构占比由4.0%降至2.5%。研究表明，基于概率统计模型的风险防范策略能够有效压降到达强度、损失规模与结构暴露。

Abstract: E-commerce transaction risks involve multiple links including payment, credit and logistics. Traditional control methods based on empirical rules struggle to balance sensitivity and robustness between normal business scenarios and peak business scenarios. This model mainly constructs transaction risk modeling for the non-promotion normal period. For peak scenarios such as Double 11 Shopping Festival, an extended framework based on scenario switching is proposed. Taking 1 million publicly disclosed transactions of an e-commerce platform in 2023 as samples, this paper constructs a joint probability risk model based on the Poisson distribution, normal distribution and Copula function, and puts forward dynamic early warning and hierarchical control strategies. Descriptive statistics show that the monthly frequency mean of payment risks is 12 with a variance of 12.8, which is close to the assumption of Poisson distribution; the skewness of credit loss is 0.35 and the kurtosis is 3.1, basically conforming to the characteristics of normal distribution. Further empirical results of goodness-of-fit test verify that the Poisson distribution and normal distribution can serve as effective approximations to describe the monthly frequency of payment risks and the central interval of credit losses under normal conditions. After introducing the Copula function for dependency diagnosis, the t-Copula is adopted to characterize the symmetric tail dependence structure of the three types of risks. After the implementation of the strategy, the intensity of payment risks decreased from 12 times per month to 8 times per month, the average single credit loss dropped from 500 yuan to 350 yuan, and the proportion of logistics risk structure fell from 4.0% to 2.5%. The research indicates that the risk prevention strategy based on probabilistic statistical models can effectively reduce risk occurrence intensity, loss scale and structural exposure.