The article is titled "Understanding FinTech Lending!"

1. How does FinTech lending work?

In traditional lending models, lenders decide whether to lend to a business based on three considerations: the borrower's personal interests in the business, whether the business can provide sufficient collateral, and an evaluation of the business's operational information, including its financial status, profitability, and past borrowing experience. In this model, traditional lending methods are limited when dealing with startups or unsecured loans. The lack of information symmetry leads to a decrease in the enthusiasm of financial institutions to participate, and small and medium-sized businesses face a large credit gap.

The emergence of FinTech has greatly alleviated this problem. We define FinTech lending as lending activities driven by electronic platforms other than banks. The disruption of traditional lending by technology is mainly reflected in two aspects: improving credit assessment through a large amount of data, and using real-time information and platform advantages to execute repayment. It is with these advantages that FinTech lending has emerged in the bank-led lending market and continues to penetrate into small and micro businesses and consumer groups where bank credit supply is insufficient.

This article primarily discusses two types of FinTech lending: lending businesses entered by large technology companies and decentralized platform lending represented by P2P.

In terms of pre-loan credit assessment, large tech companies have a significant advantage. Large tech companies accumulate a large amount of account activity data through e-commerce platforms, mobile terminal applications, and other channels. They analyze the existing data to generate user profiles, thereby making credit scoring more accurate and comprehensive. At the same time, they use network effects to expand other user services, forming a feedback loop of data networks. This data includes real-time updated information such as social software records and digital footprints, which allows credit scores to be adjusted quickly and dynamically.

All of this is due to the ability to aggregate and analyze data given by technology. An example is Mercado Libre in Argentina, which claims to analyze more than 1,000 pieces of data in a loan process. Another example is the State Grid Commercial Bank in our country, which claims to form more than 100,000 common indicators and more than 100 risk control models based on the operational data of small and micro customers, and more than 5,000 risk control strategies. On this basis, a full-process automatic operation system from customer admission, risk assessment, marketing pricing to real-time lending, and post-loan monitoring has been built.

In terms of executing repayments, the ecosystem built by large tech companies is one of their guarantees, and it can even play the role of "data collateral". Through payment accounts, tech companies can directly deduct interest and other related fees from the user's income account, and defaulting accounts may be disabled in the ecosystem built by large tech companies, thereby reducing moral risk. For example, Amazon provides up to $750,000 in loans to platform merchants, but at the same time stipulates in the terms that the borrower agrees to authorize Amazon to repay the loan by deducting its income on the platform, which means that the merchant's repayment priority to Amazon is higher than other creditors. In addition, considering the high degree of penetration of large tech companies in daily consumption, business activities, and social groups, this is also an effective way to reduce overdue.

Another advantage of "data collateral" is to weaken the effect of the "financial accelerator". Due to the cyclical fluctuations in the price of collateral-like assets, traditional lending often has strong pro-cyclical properties, while data within the online ecosystem is less sensitive to economic cycles. A working paper by BIS (Gambacorta, etc., 2020) mentioned that the credit granted by large tech companies is not significantly related to housing prices and local economic development levels, but is highly sensitive to specific corporate information, such as transaction volume, and social attributes within the tech company ecosystem. Moreover, the credit to online companies has no obvious relationship with the economic environment where the company is located, which means that as long as the online demand is guaranteed, the credit can continue to be executed. This has to some extent enhanced the stability of credit supply.

The essence of platform-based lending is that tech financial services facilitate transactions between the supply and demand of funds. For example, the role of P2P platforms in lending is to match borrowers and investors, but the platform itself does not bear the risk. Before the loan, P2P platforms usually provide a low-cost standardized loan application process, publish relevant information about the borrower's financing needs and financial status, and after the investor's review, the borrower and the investor directly match and sign a loan contract. Most platforms provide borrower screening and loan pricing services, grade their credit to determine loan interest rates. After the loan, the platform acts as an agent for the investor, and the services provided include keeping loan records, collecting repayments from borrowers, distributing cash flow to investors, pursuing overdue debts, etc., and obtaining continuous service fee income.

P2P platforms themselves are not providers of funds, and their main income is service fees generated by expanding the investor base, rather than net interest margin, essentially belonging to credit intermediaries. In order to attract investors, platforms often promise to guarantee investor returns in the early stages, and defaulted loans will be included in the platform company's liabilities, which may lead to maturity mismatches in the platform company's balance sheet and trigger runs. This is a risk that cannot be ignored in the platform lending model.

2. How has FinTech lending progressed?

With a large customer base, high brand recognition, and the use of cutting-edge technology, large tech companies have a good foundation for developing financial businesses. From 2015 to 2020, China's online retail sales of goods and services soared at an annual growth rate of 24.8%. Under the catalysis of the prevention and control of the new crown epidemic, the online economy further fermented and heated up. By the end of 2020, online retail sales of goods and services had accounted for 30% of total retail sales of consumer goods. At the same time, China's mobile payment system has also been developed. Initially, the payment function of the e-commerce platform was set up to solve the lack of trust between the parties to the commodity transaction, and then it turned into the basis for large tech companies to enter FinTech lending. According to iResearch data, China's third-party mobile payment transaction amount in 2019 was about 226.2 trillion yuan, about 2.3 times the GDP of that year.

The electronic payments involved by large tech companies often have the characteristics of small amounts and high frequency, which is consistent with the transaction characteristics of e-commerce platforms. According to statistics from the People's Bank of China, in 2019, the number of payments on the Internet Clearing Platform was 3975.4 billion, accounting for 69.9% of all payments, but the payment amount accounted for only 3.8% of all.

With the support of a large customer base in the payment system, the FinTech loan business has also been fully rolled out to the customer group facing electronic payment. In terms of loan issuance, the existing infrastructure such as electronic payment of large tech companies keeps the marginal cost of tech companies low, and they do not have to pay the cost of adding outlets and staffing like traditional financial institutions, thus forming a huge advantage of low-cost mass customer acquisition. Because of this, with the diversion of e-commerce platforms and payment systems, FinTech lending led by large tech companies has been able to penetrate into the weak areas of bank credit, and at the same time enhance user stickiness with the advantage of more efficient and convenient processes.

In recent years, FinTech lending has rapidly expanded globally, with particularly rapid growth in Asia and Africa and Latin American countries. From 2014 to 2019, the annual growth rates of credit granted by global large tech companies and FinTech companies were 94.5% and 68.0%, respectively, reaching 572.2 billion US dollars and 223.3 billion US dollars in 2019. Looking at the countries, China's FinTech lending leads the way in terms of total amount or per capita quota. In 2019, the total amount of FinTech lending (the sum of the two types of FinTech lending) accounted for 78.8% of the total global statistics, and the per capita FinTech lending loan quota reached 448 US dollars, much higher than the overall average of 118 US dollars. Looking at large tech company lending and platform lending, the UK and the United States have faster development and dominate, while China and Japan are the opposite. In 2018, the total amount of credit from large tech companies worldwide exceeded that of platform lending, and it grew rapidly with a year-on-year growth rate of more than 90%, indicating that the concentration of the FinTech lending market is getting higher and higher.