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论文编号：	13718
作者编号：	2320180846
上传时间：	2022/12/7 7:25:32
中文题目：	M公司网络货运平台税收风险管理研究
英文题目：	Research on tax risk management of M company''s network freight platform
指导老师：	石鉴
中文关键字：	网络货运；税收风险；风险识别与对策
英文关键字：	Network Freight；Tax Risks；Risk Identification and Countermeasures
中文摘要：	我国一直是运输大国：货运总量大，道路运输需求多，幅员辽阔公路里程长，货车和货运司机群体庞大，为我国物流业的发展提供了基础。但我国不是物流强国，道路货物运输个体散户偏多，货源垄断共享能力差，难以形成集群效应，且运输过程中散户需承担更大风险，物流企业现金流动性差，对于货运整个过程的监管不完善。随着互联网技术的不断发展，网络货运平台应运而生，利用大数据和平台技术对道路运输货物和道路运输运力进行合理分配和组合，在一定程度上解决了传统道路运输存在的运营规模小、获取发票困难、服务质量差的问题。但网络货运平台在运营的过程中也暴露了一些新的问题，这些问题主要集中体现在税收风险上，互联网技术在给网络货运平台带来信息和技术的便捷的同时也带来了订单虚构、订单导入换取发票、承运人司机证件造假、货物信息不一致、运费结算没有实际发生、缺少承运合同的新的问题。 本文结合网络货运的相关定义，以及网络货运的相关税收政策，基于风险管理理论，查阅相关文献，通过实地走访考察N市网络货运平台龙头企业M公司调研当前风险防范机制，与网络货运平台相关从业专家和T市税务专家座谈，通过座谈对M公司网络货运平台的风险点进行总结，并通过发放调查问卷对总结识别的该网络货运平台税收风险点进行评价。利用模糊综合分析法，结合走访座谈和调查问卷数据，对M公司网络货运平台的税收风险点进行量化，计算相关权重值，搭建了网络货运平台税收风险评估模型，以对网络货运平台存在的税收风险因素进行识别、评估和防范。 本文的创新点在于不同一般对网络货运平台风险研究的全盘分析，首次以网络货运平台的税收风险做为研究对象，分析其产生的原因和表现形式，总结识别风险建立了专门针对于税收风险的评估模型。同时与之前的税收风险研究相比更具有针对性，之前研究更多的关注宏观的税务指标，更关注纳税额和营业收入间存在的逻辑关系，而本文在识别税收风险指标时充分考虑到产生风险的根本因素：大量的业务数据，通过在源头遏制风险，在当前大数据飞速发展的背景下更有实际意义的。
英文摘要：	China has always been a big transportation country, with a large amount of freight, great road transportation needs, a long territory highway mileage, and a large community of freight and freight drivers, which provides a solid foundation for the development of Chinese logistics industry. However, China is not a strong country in logistics, as a majority of individual investors in road freight transportation make it hard to form monopoly, and thus their goods source sharing ability is weak, leading to it difficult to form a cluster effect. Moreover, individual investors must bear greater risks in the transportation process while logistics enterprises have poor cash liquidity, and the supervision of the whole freight transportation process is not perfect. With the continuous development of Internet technology, network freight platforms arise at the historic moment, which adopts big data and platform technology to reasonably distribute and collaborate road transport cargo and road transport capacity, solving the problems existing in traditional road transport to a certain extent, including small operation scale, difficulty in obtaining invoices and poor service quality. However, some new problems have been exposed in the operation of the network freight platforms, which are mainly reflected in the tax risks. While the Internet technology brings convenient information and technology to the network freight platform, it also gives rise to some new problems, such as false order, orders placed for invoice, certificate fraud of carrier drivers, inconsistent cargo information, no freight settlement, and lack of transportation contract. In combination with relevant definitions of freight and tax policies related to freight, on the basis of the theory of risk management and relevant literature, through field visits to investigate the freight network platform of a leading enterprise M in N city to research current risk prevention mechanism, with information discussion with experts of freight network platform and tax experts in T city, the risk points of the network freight platform are summarized. In addition, the tax risk points of the network freight platform identified by the summary are evaluated by issuing questionnaires. Utilizing the fuzzy comprehensive analysis method, combining the data of interviews and questionnaires, the tax risk points of the network freight platform are quantified, the relevant weight values are calculated, and the tax risk assessment model of the network freight platform is constructed, so as to identify, evaluate and prevent the tax risk factors of the network freight platforms. Different from the general comprehensive analysis of the risk research of network freight platform, the innovation of this thesis is that it takes the tax risks of network freight platform as the research object for the first time, which analyzes its causes and manifestations, summarizes and identifies the risks, and establishes a special assessment model for tax risks. At the same time, it is more targeted than previous studies on tax risks, which paid more attention to macro tax indicators and the logical relationship between tax payment and business revenue. Instead, the fundamental factors generating risks when identifying tax risk indicators are fully taken into account, namely, a large amount of business data. It is also of practical significance for containing risks at the source in the context of the rapid development of big data.
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