Bairong Closes Trust Gap between China’s Low Credit Borrowers and Risk-Averse Lenders
COVID-19 and China
PHOTO: Credit to Joshua Hoehne from Unsplash

We believe intelligent risk management is by far one of the most mature applications of AI/ML in the financial sector. For years, families with low income and SMEs have been dismissed by Chinese banking systems and have trouble accessing personal and business loans. In recent years, financial institutions have been much more open to these underserved communities in order to fuel their loan growths.

When it comes to clients with little credit history, financial firms lack data and technical expertise to make credit decisions. Fintech firms came forth to fix this issue by powering the entire lending process with AI-driven risk management abilities, with Bairong and Tongdun counting among the representatives. This week, we held an exclusive interview with Ji Yuan, Chief Risk Officer of Bairong, and spoke about the recent developments in both Bairong and the fintech sector.

Bairong shifts its strategic focuses along with the broader sector

The Peer-to-Peer (P2P) sector used to have a rosy outlook thanks to the rise of mobile and online channels to deliver financial products and almost half of Bairong's revenue was contributed by the Internet finance companies before 2017. But the Ponzi schemes, defaults, questionable business ethics and poor corporate management that ensued a wave of shutdowns in 2017 and negative publicity about the industry has led to drastic regulatory actions.

Nevertheless, the borrowing needs of low-credit individuals remained unmet. Traditional financial institutions could meet such demands with a much lower interest rate than the P2P lenders. Hence, Bairong started to help traditional financial institutions establish risk management platforms so that they could extend loans to those groups of borrowers. For now, around 80% of Bairong's revenue comes from traditional financial institutions. We believe it is a healthy proportion as legacy financial firms have much stable business models and lower regulatory risks compared to Internet finance companies.

In addition, as consolidation continues in the P2P sector, operators are actively seeking maximized returns on the back of stringent caps on leverage ratios and interest rates, and Bairong can come in and demonstrate its value.

Regulation remains a risk, but can surely be mitigated

A fintech company specializing in intelligent risk management also needs to be cautious about managing its own risks, which mainly stems from tightened regulatory scrutiny. Generally, regulatory reforms will not happen overnight and regulators will allow companies to adjust their strategic focuses within a specified period of time. Hence, the key question is whether the fintech service providers can come to grips with the regulatory trends in the financial sector, and adjust their services accordingly as well as educate their financial clients to transform business models.

For example, we believe Bairong has done a great job in mitigating its own risks. After 2017, Bairong paid more attention to complying with data collection and usage, cleaning up some data and involving law firms in data compliance procedures. The company has also been frequently communicating with supervisors to gain insights on the regulatory outlook.

Product ecosystem is an edge

Bairong offers a wide range of products to power the entire loan process with AI-driven risk management abilities. Below is a brief on how it helps lending platforms prevent risks and capital losses in pre-loan, in-loan and post-loan scenarios.

Pre-loan: credit decision making. Bairong leverages machine learning capabilities to design different credit models and analyze suspected fraudulent activities on the entire network; as a result, the system can predict whether the applicants will default in the future and provide clients with personalized credit decisions (whether to lend or not, how much and in what ways) with risk-based pricing.

In-loan: real-time monitoring. Bairong tracks borrowers' repayment behaviors and issues prompt alerts if the system detects material risks such as deteriorated credit scores, new late payment records on other platforms, legal disputes, invalid mobile phone numbers and changes of usual address.

Post-loan: overdue debt management. Bairong offers various repayment reminder notices including robotic bill collection calls to automate the previously labor-intensive process.

We believe launching an ecosystem is stronger than selling a standalone product, as financial organizations will find it hard to integrate different systems if they use the anti-fraud services, identification verification and overdue debt monitoring supplied by different vendors.

Amassing data is more about collaboration nowadays

To determine how likely a borrower is to default in the future, Bairong utilizes cross-industry data. Previously, the company accumulated massive data sets when it was still part of Baifendian, a big data platform which was founded in 2009. At present, there are generally two ways to acquire data: by purchasing directly or integrating from thousands of enterprises including banks, mobile operators, e-commerce sites, social websites and so on. We believe it is ideal for a fintech company to accumulate proprietary data and maintain long-term, stable data partnerships. Therefore, it can consistently source data from diversified parties and will not experience high volatility if one or two vendors choose to terminate the partnerships.

Pricing model depends on the forms of services

Standardized products which are delivered in the form of API or SDK are priced in a clear, straightforward manner. For example, the client may pay based on the number of calls ("API per call") or be charged an annual subscription fee (fixed quota). Most fintech solution providers prefer standardized products due to higher margins than customized products and services. It is easy to scale and fast to deliver.

For customized products, providers will charge service fees based on contract terms. Most state-owned large financial institutions, which generally have complicated lines of businesses and heightened security concerns compared to smaller organizations, are interested in receiving a complete set of solutions that are hosted on-premises. Since the fintech solution providers are mostly startups with a few years of operating history, their pricing power is relatively weak when they serve established financial institutions. Nevertheless, we believe the solution providers still expect to work with those organizations when offered non-monetary incentives, such as building brand awareness and serving stable clients.

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