[Editor’s note: This is a guest post from Thomas Wang, Director of Data and Science Analytics at China Rapid Finance. China Rapid Finance is a Platinum Sponsor at LendIt USA 2016, which will take place on April 11-12, 2016, in San Francisco. Various members of the China Rapid Finance team will be in attendance and speak at LendIt USA.]
In the U.S. and other developed markets, lenders have the advantage of industry standard credit scores, such as FICO, to assist in their credit decisions. Coupled with other traditional credit data, FICO conveys a borrower’s willingness and ability to repay loans as well as their financial stability. These three characteristics are crucial in predicting a borrower’s credit behavior. However, according to the People’s Bank of China (the PBOC), as of the end of 2014, there were approximately 500 million individuals with quality employment records but no credit history. This gives marketplace lending industry little official data support to screen and select good borrowers, but China Rapid Finance Limited (CRF) has developed a scalable pre-approval mechanism utilizing its predictive selection technology, which enables it to actively seek quality borrowers.
Open-application is the most widely used mechanism of acquiring borrowers in China by marketplace lending platforms. Nevertheless, many open application models have issues with high rates of fraudulent applications. Unlike with the open application model of borrower acquisition, with the pre-selection model, potential borrowers are contacted by the marketplace lending platform rather than the borrower seeking out the marketplace lending platform. Thus, the pre-selection model has the advantage in that marketplace lending platforms are able actively seek out potential borrowers with desired characteristics rather than relying on the accuracy of applications submitted by potential borrowers, which have a higher likelihood of fraud.
We are the only marketplace lending platform in China to use predictive selection to acquire customers on a massive scale at a low cost. Imagine you’re young and employed at a small company. You have a quality employment record and a mobile footprint. However, the only credit options available to you are egregiously expensive. Issues like these affect 500 million Chinese who do not have access to basic consumer credit. We call these Chinese individuals Emerging Middle class, Mobile Active consumers, or EMMAs for short. To remedy this issue, China Rapid Finance applies its proprietary predictive selection technology, coupled with a unique business model, to offer easy to understand, affordable credit on a massive scale with a low borrower acquisition cost.
We have established cooperation relationships with leading companies in search and social media in China. When evaluating a potential borrower for our marketplace lending platform, we consider the online behavior data and the previous borrowing behavior (if any) on our marketplace. Our pre-selection of potential borrowers for loans facilitated on our marketplace lending platform helps to remedy issue of fraudulent applications by only servicing those individuals that we have previously identified as having desired characteristics.
Our technology, which we developed over our 15 years of experience as a credit analytics service provider and marketplace operator, utilizes distinct algorithms for loans with different sizes and maturities to analyze vast amounts of data. We continue to source data from diverse channels, including potential borrower applications, big data accessed through from our cooperation partners and credit data created by borrowing behavior on our marketplace. Each of our credit scoring and decisioning algorithms utilizes hundreds of variables to assess and make intelligent and rapid consumer credit decisions on a large and growing scale. Our technology continues to evolve through machine learning as it scales to allow for more informed and intelligent decisions based on credit data created by borrowing behavior on our marketplace. Using our proprietary technology, and more specifically, our predictive selection technology, we are able to identify potential borrowers who should receive pre-selection status for loans facilitated on our marketplace lending platform.
Once the pre-selected borrowers are identified, we offer these potential borrowers small loans up to 500 RMB ($80). As EMMAs begin borrowing and repaying, their repayment behavior patterns are noted and stored by our automated decisioning technology (ADT). Our ADT compiles and analyzes this behavior data, and if the borrower has had a positive repayment profile, we gradually increase that borrower’s credit limits. When borrowers on our marketplace have developed a sufficient credit history on our marketplace, we invite them to one of our 100 local data verification centers where they can apply for larger loans facilitated on our platform. This simple process involves verifying additional data, including physical data, such as housing and employment records.
In conclusion, the lack of credit data, coupled with high levels of fraud, makes it difficult for institutions to assess borrower’s credit characteristics in China. However, with proprietary predictive selection technology, we are able to efficiently identify potential borrowers with attractive characteristics, and offer these potential borrowers easy to understand and affordable loans that are facilitated on our marketplace.
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