BiRank与PageRank：在公司注册数据上使用SNA进行财政风险预测
BiRank vs PageRank: Using SNA on Company Register Data for Fiscal Risk Prediction
摘要：
高效的财务管理需要确保所有税务主体的合规行为，而不会产生过多的人力成本或阻碍合规公司。要做到这一点，准确预测违规或欺诈行为至关重要。社会网络分析（SNA）为欺诈预测提供了强大的工具，因为欺诈通常集中在现实世界社交网络的某些区域。在本文中，我们提出了我们的结果比较PageRank和最近的BiRank推断风险等级的基础上网络结构和先前的欺诈信息。具体来说，我们从公司注册数据中建模我们的社交网络。我们发现，在这种情况下，研究BiRank优于PageRank在质量的欺诈预测和运行时间的排名。结果表明，这类算法通常用于欺诈和风险预测，更具体地说，也说明了BiRank的潜力，因为它开辟了新的建模机会。我们的研究结果表明，选择公司进行税务审计的基础上BiRank产生的精度为16.38%的前20.000主题选择83.4%的所有欺诈案件（召回）。
英文摘要：
Efficient financial administrations need to ensure compliant behavior of all tax subjects without excessive personnel costs or obstruction of compliant companies. To do so, accurate prediction of non-compliance or fraud is crucial. Social Network Analysis (SNA) provides powerful tools for fraud prediction as fraudulence is often clustered in certain areas of real world social networks. In this paper we present our results of comparing PageRank and the more recent BiRank to infer risk-ranks based on network structure and prior fraud information. Specifically, we model our social network from company register data. We find that in this case study BiRank outperforms PageRank in both quality of the resulting ranks for fraud prediction and run time. The results show that this class of algorithms is generally useful for fraud and risk prediction and more specifically also illustrate the potential of BiRank in comparison, as it opens up new modeling opportunities. Our results show that selecting companies for tax audits based on BiRank yields a precision of 16.38% for the top 20.000 subjects selecting 83.4% of all fraud cases (recall).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
