Credit big data is very important to online lending, after all, online lending is basically based on big data, platform risk control. If the credit big data reflects the existence of bad behavior of the user, personal credit is not good, then it will have a certain impact on the approval of online loans.

What is the credit score big data query?

Generally speaking, the current credit system data in China is mainly judged from the data of various countries and or financial institutions plus, for example, public **** agencies. And what is big data credit? At present, there is no recognized definition of big data, the general view is that big data refers to the amount of information involved is so large that it can not be achieved through the current mainstream software tools, in a reasonable period of time to capture, manage, process, and organize the information into a service for business decision-making. Simply put, for example, the e-commerce industry such as Taobao, ** e-commerce to make judgments on consumer data information is big data credit. They and some third-party Internet financial institutions have their own reliable big data credit sources.

What is the difference between traditional credit collection?

From the type of view, the traditional credit collection company adopts the peer information sharing model, that is, the customer inquires about a piece of information needs to first *** enjoy a corresponding information; while the Internet company is to utilize its own massive data advantage and user information, from wealth, security, compliance, consumption, social and other latitude to judge, to establish a credit report for the user, the formation of a large data The company's credit report is based on a massive database of big data.

It is worth mentioning that the traditional credit collection model faces problems such as incomplete credit data, low motivation of platforms to upload data, untimely updating, and high access threshold. The advantages of the big data credit model lie in the wide range of data sources, to make up for the shortcomings of the traditional credit coverage; data types are diversified, not limited to credit data, and more comprehensively reflect the individual's credit situation. The difficulties lie in the data clutter caused by too much information, the difficulty of integrating data from multiple parties, and the need for data correlation analysis for a longer period of time and practice to test the accuracy of credit evaluation data in the short term. In addition, big data credit is also facing legal risks, and it is difficult to control the protection of personal privacy.