The effect shipper, carrier, and supervisory management data mining of online freight platforms on the innovation of financing model for Chinese financial institutions

Abstract

The application of e-commerce and digitization in the logistics industry has given rise to the emergence of online freight forwarding platforms that connect shippers and carriers for the efficient transportation of goods. Online transactions on the platforms generate a large amount of data, creating opportunities to gain valuable insights through data mining techniques. However, against the backdrop of the Chinese government's accelerated efforts to create a new three-dimensional dynamic logistics landscape of "Internet + Logistics," the traditional financial service models of Chinese financial institutions are struggling to meet the evolving needs of these platforms. This research and innovation project explores the impact of data mining on the operation of online freight transportation platforms and the development of innovative financing models by Chinese financial institutions. A mixed research methodology was used to collect primary data, combining quantitative analysis, qualitative questionnaire analysis, and relevant interviews to collect and analyze the data. By analyzing financial data such as transaction volumes, creditworthiness indicators, and risk factors on these platforms, various data mining algorithms are utilized to mine and predict models. Further evidence is presented to analyze the relationship between shippers, actual carriers, freight platform managers, and financial service innovations that transact through online freight platforms and to propose new types of financing service solutions that can support the growth and sustainability of the logistics industry. These financing models have the potential to increase financial inclusion, reduce credit risk, and facilitate capital flows in the freight industry.