Yixin pleasant credit to white-collar special enjoyment services

At present, although there are thousands of P2P platforms in China, most of which are mainly mortgage loan services for small and micro enterprises, there are few platforms in which they can simply make personal credit loans. The credit-based peer-to-peer lending platform, a pleasant loan on the line at the very beginning of the very definition of the platform services platform for the city's white-collar workers to provide unsecured unsecured credit loan advisory services.

I think we all know that if the loans are made by small and micro-enterprises in the mode of mortgage and the single loan amount is high, it is very easy to achieve great transaction scale. Moreover, the risk control model of the loan under the line of mortgage Domestic now is also more mature. So, in the end why the pleasant loan will lock the target audience to white-collar workers? And how is the pleasant loan online risk management for white-collar workers do?

The first thing we need to know is that actually there is a huge demand for borrowers in white-collar workers. According to the loan data, white-collar borrowers are mainly aged 25-35 years old and are therefore in the stage of wealth accumulation, while most of them are non-mortgageable assets. However, at this age group But also faced with many rigid large consumer demand, such as marriage, car purchase, decoration, tourism, training and so on. This part of the population borrowing needs can not be resolved through the traditional financial services agencies, so this is a very potential market for P2P platform.

Second, the white-collar workers repayment ability and repayment will be relatively high quality. Pleasant credit for white-collar borrowers qualification requirements are between the age of 22 to 55 years of age, after-tax punch card salary of 4,000 yuan in the current unit of continuous work for six months, no bad credit history. Generally speaking, white-collar borrowers meeting the above conditions are generally all graduates with a bachelor's degree or above, have a good sense of creditworthiness, and have genuine borrowing needs. Therefore, Loans for Loans filter.

Not only that, in line with the basic conditions of white-collar workers to submit a loan application, the loan will adopt a unique big data credit model of the borrower's qualifications for the first round of automated filtering, data analysis, the borrower's income level, debt ratio And credit records for evaluation screening. This greatly saves the time and cost of auditing, and improves the efficiency of auditing. Allow eligible borrowers to get funding faster.

Of course, the manual review process is still an essential part. Credit loan auditors will be happy through cross-validation and other means of credit information to determine the qualifications of borrowers to give the final audit results.

It is easy to see that doing pure credit loan consulting services on a pure line needs to be analyzed based on a scientific and effective big data wind-control model, and the accumulation of big data requires sufficient trading experience to precipitate. Based on its eight years of experience in customer service, Credit Suisse has accumulated a large amount of effective credit data and can make reasonable judgments on the qualification of customers in combination with different online and offline data types. Through the innovation of big data, it forms a core competition in risk management force.

Insiders pointed out that the wind control model data will be a major trend in the future development of P2P, which in fact is an invisible threshold for P2P industry, for the establishment of P2P platform is almost impossible to achieve.