Shulou(Shulou.com)06/03 Report--

P2P network loan ("peer-to-peer") provides convenient financing channels for small and medium-sized enterprises and individuals. In recent years, with the gradual development of Internet finance, P2P network lending has become a hot new model of Internet finance. With the financial advantage of "high yield", all kinds of P2P network loan platforms are growing year by year. At present, the number of normal operating P2P platforms in China has reached nearly 3000, with a cumulative transaction volume of about 800 billion yuan.

However, with the explosive growth of the online loan industry, there are also major risks. Platform problems such as high-interest-rate Ponzi schemes, fake targets, platform self-financing, money laundering, misappropriation of funds, false financial performance, and running away continue to occur. The news of overdue repayment of large platforms and running away of small platforms is not uncommon, causing huge losses to investors and a crisis of trust. Cause heavy losses to investors. At present, the biggest risk faced by P2P online lending is credit risk, which has become the main bottleneck in the development of P2P online lending industry, and borrower credit evaluation is the decisive factor to reduce credit risk and improve the level of enterprise risk management.

The related research on credit evaluation of P2P borrowers at home and abroad is mainly discussed from two aspects: evaluation indicators and evaluation methods. Because of the relatively complete data sharing credit information system in foreign countries, the foreign research on borrower credit evaluation includes not only the borrower information of the platform itself, but also other platforms such as government credit system, social network, shopping behavior and so on. However, most of the domestic researches on P2P network loan only introduce the operation mode of the main P2P network loan platform at present, or only carry on the risk research for the specific platform, and there is not a unified standard for the evaluation index.

Therefore, although there are dozens of indicators applied to the credit risk assessment of borrowers, except for a few indicators for the personal information of borrowers, other indicators used by different P2P lending platforms are not the same. in the study of credit risk, due to the use of different indicators, the conclusions are not the same, it is difficult to give a clear relationship between indicators and risk. As for the credit risk assessment of borrowers, there are few reports on which basic indicators are needed as evaluation criteria to explain the comprehensiveness, versatility and credibility of the evaluation.

On the other hand, with the continuous increase of data quantity and data types, the data volume of P2P network loan platform has reached the level of PB, EB and even EB. With the continuous access of data from bank credit system and other sharing systems, it is inevitable to apply big data technology to platform data management and analysis. At present, the vast majority of borrower credit risk assessment studies are based on the results of static analysis, but the data are changing, and the risk is generated in the process of change. From a dynamic point of view, it is of more practical significance to analyze and monitor the borrower's credit risk in real time.

Based on the above reasons, this study applies big data technology to establish a real-time monitoring model and risk control scheme for borrowers' credit risk, which provides big data architecture reference for borrowers' credit risk assessment on P2P network lending platform.

The overall framework of the research content is shown in figure 1:

Figure 1 the overall framework of the study

The architecture of big data's real-time monitoring model is shown in figure 2:

Fig. 2 big data real-time monitoring model framework

The technical route of the research is shown in figure 3:

Fig. 3 Research technical route

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