Data Interface Analysis of Small Loan Risk Control in renrun cloud region (multi-headed lending and zhima credit)

For small-loan platforms on the internet, credit investigation and risk control are important links in the Process of business development. The online small loan business mainly defends against fraud risks and credit risks, such as borrower fraud by means of cash, forgery, fraudulent use, and malicious overdraft. In addition, the information between the platform and the platform is not transparent, and users repeatedly borrow money on multiple platforms and other undesirable phenomena often emerge. The market data sharing mechanism needs to be improved.

 

The data commonly used for risk control of small-loan networks can be roughly divided into four types: Information Verification, verification of the authenticity of the borrower's identity, including identity authentication, bank card authentication, and anti-fraud, including various data such as blacklists, multi-headed lending, violation of laws and regulations, credit performance, loan records, lending details, overdue information, and user portraits, resident cities, consumption levels, operator calls, and other data.

 

Taking renrunyun's big data risk control as an example, we collected over 100 data interfaces including bank card elements, zhima credit, court untrusted personnel query, court enforcers query, multi-headed loan report, and anti-fraud, it also provides Big Data risk control methods such as "big data user behavior profiling", "Custom risk control model", and "decision engine system" based on the actual situation of the network small loan platform, propose constructive solutions to help small online lending institutions comprehensively control various financial risks.

 

In addition to providing fast query services for the Risk Control SaaS platform, renrun cloud has launched the "big data user profile report" and "risk control report" products thanks to its powerful data processing and analysis and modeling capabilities, A multi-dimensional risk control system is built based on risk control rules. In terms of anti-fraud of Small-loan networks, we analyze and identify the causes of fraud based on actual situations, and then make corresponding policies and models for different types of fraud to form a complete risk control solution. In terms of multi-headed lending, we will give full play to our advantages and use user portraits to accurately determine the borrower's resident location information, recent behavior, and user app usage information, it helps the online small loan platform to precisely control user risks before, during, and after lending.

Data Interface Analysis of Small Loan Risk Control in renrun cloud region (multi-headed lending and zhima credit)