The growth rate of various loans in China has been above 13% in the past few years, according to various sources of data. In 2018, the growth rate of commercial banks' loans for small and micro-sized enterprise (SME) reached 21.79%. In 2019, the China Banking and Insurance Regulatory Commission（CBIRC）required the outstanding balance of SME loans of state-owned large commercial banks to rise by more than 30% from the beginning of the year, and asked city commercial banks to better serve SMEs in lower-tier cities. To reach these goals, fintech has become a powerful tool to drive banks’ retail financing, serve SMEs in small towns, and control credit risk.
The fintech solution can help banks improve their SME financing by restructuring credit approval process for SMEs, lowering operation cost with data-driven decisioning, and multi-dimensional and dynamic customer profiling.
The Chinese government has been encouraging banks to implement inclusive finance and support the development of SMEs in recent years. However, SMEs still face difficulty in obtaining financing, as well as a high cost of financing. The key issue is the information asymmetry between SMEs and banks.
SMEs usually have high-frequency demands for micro-financing, so the demand and market size are undoubtedly large.
However, from the perspective of supply, traditional financial institutions such as banks need to evaluate credit risks with a series of specific financial and non-financial indicators from risk control considerations. Such SME data is not as complete and standardized as for large enterprises, so it costs banks a lot to do risk assessment. These high cost, plus the complicated lending processes, are too much of a burden for banks to make a profit.
Therefore, if the problem of information asymmetry could be solved, and banks did not have to follow the traditional process, then a huge amount of costs could be saved and SME financing would experience explosive growth. From this point of view, solving the problem of information transparency with fintech solutions will greatly improve financing for SMEs, and activate the retail business of banks.
The voice of the market: the “unacceptable” SMEs
There are many types of SMEs, including small franchised restaurants, milk-tea shops that support third-party payment, and mobile pancake stalls, to name just a few. The smaller the enterprise is, the more challenging it is for banks to provide financing. Banks face a lot of difficulties when handling SMEs loans, such as scattered data, high risks, high costs, and low profit. Therefore, SMEs loan volumes have always been relatively small and hard to approve.
1. Self-employed individuals not accepted
Self-employed individuals, one typical form of SME, pose the most difficult challenges for banks.
Suppose a pancake stall owner “Aunt Ge” is applying for a bank loan. Her only realizable asset is her mobile pancake vehicle, the value of which would not be recognized by banks. Her "enterprise" has no government-approved registration, and her daily accounting is recorded manually. She has no credit history in the central bank, and it is impossible for her to apply for a credit card. The cruel reality is that her loan application is unlikely to be accepted.
2. Micro shops also rejected
Now suppose “Aunt Ge” has got a small store with a business license, limited registered capital, non-standard tax practices, and some accounting records. She will still very likely to be rejected if she wants to borrow RMB50,000 from a bank to decorate her shop, since the cost of providing the loan is higher than the profit for the bank. The costs for the bank included customer reception, investigation, data entry, initial review and final decision. How about if she wants to borrow RMB200,000? Traditional banks will think it’s too risky to lend RMB200,000 yuan to a small shop.
For these reasons, SMEs like “Aunt Ge” tend to think that banks are snobbish and dislike self-employed individuals.
Banks: the cost is too high
It’s not true that banks dislike self-employed individuals.
It’s logical that banks pass up this business if the net present value (NPV) of such business is negative, which means non-profitable. It is understandable that commercial banks consider the issue from their business perspective. Even with policy encouragement, they feel helpless facing the potential huge demand with terrifying cost.
In the past, there were three risk control models for banks to handle SMEs loans: the guarantee model, IPC model and credit factory model. In the guarantee model, SMEs need to provide mortgages and guarantees. Many SMEs are unable to do so. The IPC model emphasizes field investigation and information verification by experienced loan officers, and has its advantages in customer acquisition and service. But this model is prone to ethical risk. With the credit factory model, banks can handle SME loan applications in bulk like standard products, which can help improve operational efficiency. But the loan size is small, and it requires a larger team. Compared with the other two models, the credit factory model is more standardized with a relatively simple process. It can be divided into 7 steps:
The whole process will take at least a week. There may also be some special circumstances that will further delay the review process, such as the field investigation needs to be rescheduled, by request from the customer; the accounting materials are not ready; some materials have not been approved, and so on.
Although banks created the credit factory model to achieve a “standardized process”, each step needs to be done manually and it needs a manual push to the next step, which makes it difficult to speed up the process.
Operating costs also hamper efficiency improvement. The basic team requires dozens of people including sales, data entry, pre-review, operation, and approval staff. They can handle at most 20 applications per day. If they want to double the business, the team size also needs to be doubled. As there are too many applications, customers have to wait till the next or the third day before their applications can be accepted.
Because of the high cost, the interest income from a RMB50,000 loan could not even cover the cost, which means a loss-making business for banks.
Although customers like “Aunt Ge” should be their SME targets, traditional banks feel it’s very difficult to cover these customers.
Reconstructing the credit process with fintech
The cost of traditional methods is too high to meet the existing demand. Fintech is an important tool to lower the cost, ease the information asymmetry, and effectively increase the supply.
Fintech can improve or restructure the process for SME financing, which makes it profitable for banks to provide a loan of RMB50,000 or even lower to self-employed individuals or SMEs.
The lending process can be divided into pre-loan, in-loan, and post-loan. Fintech can play an important role in the whole lifecycle of the lending process, especially to accelerate the turnover of SME loans.
1. Pre-loan: multitasks in parallel
In the pre-loan phase, Fintech can conduct data acquisition, processing, and analysis simultaneously, which greatly improves the approval efficiency compared with the traditional credit factory model.
When Aunt Ge needed to apply for a loan in the past, she had to prepare a pile of documents and went to four or five institutions. With the help of fintech and customer authorization, Pintec and other fintech companies can help banks complete the entire process in 15 minutes, from data collection, filtering through data lake, data cleansing, to customer profiling with data mining and machine learning.
Customers’ application process has been streamlined, with the seven steps of traditional credit factory model integrated into only one step. In fact, fintech is playing a key role behind the data-driven decisioning system. There are nearly 10,000 data points collected, thousands of indicators calculated, and more than 500 characteristic variable analyzed each day.
Under this new model, the bank can complete the approval process without even seeing “Aunt Ge” in person.
2. In-loan/post-loan: real-time dynamic management
During the in-loan and post-loan phases, banks can realize real-time management of customers with the help of fintech.
In the past, banks evaluated a customer based on the static operating and financial status at the time he/she filed the application. In fact, operations of companies are changing very fast, especially for SMEs. Banks can now rely on fintech to get real-time risk evaluation based on data tracking customers’ lifecycle and dynamic changes.
Specifically, banks can monitor customers' dynamic changes in fraud risks and business risks through online data. They can monitor the post-loan business operation data based on the customer's industry.
For example, the business of “Aunt Ge” changes. She starts to sell clothes, toys, and even mobile phones, instead of operating a pancake stall. Banks do not need to check onsite to know the changes. They can learn about the change through analysis of the business data and transaction information, and understand its dynamic risk of operation.
If the business of a customer is going down and transactions are decreasing, the bank needs to be more careful when the customer is applying for a new loan.
With fintech solutions and based on customers’ industries, banks can set up a series of early warning indicators, which banks can get in real-time and use as guideline for loan collections.
In summary, fintech tools and solutions can restructure the SME financing process, and data-driven decisioning can lower the cost of operation. Even with much more loan applications from SMEs, the costs of banks to meet such demand will not change much. In addition, multi-dimensional and dynamic customer profiling can help banks better collect data, verify credit, and control risk, further driving SME financing.
Extra: Accurate customer profiling enabled by fintech
With e-commerce transaction data, logistics data and online business turnover data, it is relatively easier for SMEs to get bank financing. Some banks can leverage fintech to generate multi-dimensional profiling for SMEs, make credit decisioning and complete the approval process.
For example, for "Aunt Ge", banks can use GPS to find her location, learn and verify her business and life with her phone calls, payment location and living address. The traditional way to rely on address, rental contract, or utility bills is cumbersome and can easily be fraudulent.
E-commerce platforms and third-party payment companies are those who know SMEs best in the era of mobile payment. SMEs often do business online, and e-commerce platforms have their transaction data. In terms of offline transactions, payment companies such as Alipay and WeChat Payment have a large amount of their offline transaction information. From their databases, banks can clearly learn SMEs’ transactions, sales volumes, unit price, transaction frequency and seasonality, and make judgements on their operations by comparing with peers.