Financial thinking, also known as the "profit and loss view", is an important criterion for judging whether an economic activity can be run for a long time from the input-output ratio; For the vast majority of commercial enterprises for profit, it is the starting point and the end point of all economic activities of the enterprise.
As a product of the "professional division of labor" in the process of traditional financial online, the loan industry is facing the problem of profitability evaluation while exporting professional services such as customer acquisition, operation, big data, and risk control to financial institutions, especially in the red sea era of "stock competition", as a "capital-intensive" industry, how to reasonably plan the investment of various resources to ensure the stability and sustainability of business profitability has become a crucial issue in institutional decision-making.
In the previous article, we started from the customer acquisition side and discussed the definition of new customers and the payback cycle (for details, click "The Underlying Business Logic of the Loan Industry - Customer Acquisition"); On the basis of the above, this paper will analyze the application of "DuPont Analysis", a commonly used analysis tool for financial personnel, in the loan industry.
01
What is the DuPont Analytical?
Dismantling the loan industry from a financial perspective
The DuPont analysis method is a common financial analysis method developed and popularized by DuPont, which is essentially a comprehensive evaluation of the financial status and economic performance of an enterprise by using the internal relationship of several important financial indicators.
The starting point for the DuPont analysis is return on equity (ROE), which is one of the most important return metrics for shareholders.
This indicator can be broken down into the product of the three secondary indicators of net profit margin, asset turnover ratio and leverage ratio, which connects the balance sheet and income statement in the three tables. Further dismantling of the above three indicators can help analysts initially see how changes in the business side have an impact on the final profit, and to what extent.
From a structural point of view, the DuPont analysis is similar to the "pyramid" model, with the ROE mentioned above at the top, the next level is the net profit margin, asset turnover, and leverage ratio, and the next layer is the corresponding further downward exploration of the three indicators.
This analytical framework is progressive, and the more reports are involved, the easier it is to observe the correlation and changes of such data between industry and finance. It is also through this hierarchical structure that users can clearly and intuitively observe which items or factors have the greatest impact on the profit results, and then make corresponding optimization actions.
By introducing this method into the business analysis of the loan industry, we can obtain a panoramic business link diagram of the loan industry under the DuPont analysis system (not the DuPont analysis system in the strict sense). This is shown in the figure below.
For the loan industry, the top of the pyramid under the DuPont analysis system is the pre-tax operating profit, and then the right half of the index calculation formula is the income and cost items, which include heavy capital-credit business income, light capital credit-credit business income, and commercial value-added income. Cost items include operating costs, risk costs, and other expenses.
Heavy capital-credit business income: mainly related to the scale and pricing of heavy asset matching, including the scale of matching under the off-balance sheet risk back-up model and the scale of on-balance sheet consolidated trust and self-operated small loan matching; The off-balance sheet portion of income is affected by three factors: size, pricing to customers, and cost of capital, while the on-balance sheet portion is affected by two factors: size and pricing to clients (the trust generally has full ownership of the residual interest after the allocation of priority, similar to the off-balance sheet part).
In the short term, it may be subject to the relatively stable structure of product characteristics and customer stratification, and the price will not go down again, but 24% is a red line in the industry and a benchmark for the pricing of subsequent loan platforms.
Obviously, in the absence of much room for manoeuvre on the pricing side (the cost of funds has fallen to 6%, and the possibility of a short-term decline is not high), the growth of capital-heavy credit business income will mainly rely on the same proportion of the size of matchmaking.
Further down, the growth of scale mainly depends on the refined operation of the customer group structure (risk), channel structure (customer acquisition), conversion efficiency (operation) and other processes or links, and at the same time confirm the responsible persons of each link, complete the allocation of resources according to the strategic objectives, and finally achieve the expected scale growth.
The growth of the matching scale is mainly contributed by two types of users: new households and old households, and then the users are subdivided, one is the transaction contributed by the new account in the current month (T month), the second is the transaction contributed by the new account in the previous month (T-1 month credit account), and the third is the transaction contributed by the existing account (users who have traded before T month).
In terms of the proportion of transaction structure, judging from the data disclosed by the listed platform, the proportion of existing households, also known as re-borrowers, is as high as 90%, and the proportion of new households is generally about 10%, so the transaction scale of the lending platform during the reporting period is mainly contributed by old households.
However, this does not mean that new account transactions are not important, on the contrary, because existing customers will sleep or lose in each cycle, and if the loan platform wants to maintain the growth of MAU (monthly active users), it needs to continuously acquire new accounts or take business actions to activate new accounts, which is the source and living water for the growth of user scale, and is also the main work of the customer acquisition department and the operation department.
Capital-light credit business income: It is mainly related to the size and pricing of matchmaking, and it is different from capital-heavy credit services in that the lending platform does not need to bear the risk loss of matching credit assets under this model, and from this point of view, the credit service income under the capital-heavy model mentioned above is not the concept of net income, but the concept of gross income including risk loss (in the profit and loss structure of Chief, the income from taking risks is defined as "income from the release of guaranteed liabilities").
From the perspective of the product structure of the industry's heavy capital and light capital and the preferred choice direction of the funder, the capital-heavy credit business of the risk-based model is still the mainstream of the industry and the primary choice of the funder.
Based on this, in the choice of the "capital-light model", there is also the so-called "shaving ladder is hot", and the loan platform is willing to try, but the capital does not necessarily accept it.
In this context, in addition to the capital-light model of the head loan platform, the understanding of the model by other platforms has deviated from the concept and transaction model.
Specifically, at this stage, most platforms refer to the model of diversion of risk rejection under the capital-heavy model to other loan platforms as the capital-light operation model (generally the entire transaction closed-loop is completed in the form of API, and the user will not be exported out of the APP, so as to retain the possibility of subsequent re-recovery), that is, as long as the company does not bear the credit risk matching, it will be identified as a capital-light model, but in fact, the asset risk is covered by other lenders. At this time, the lender who provides diversion is only a customer acquisition channel equivalent to the risk-taking lender.
The true capital-light operation model should be that "the capital bears the credit risk", and the lending platform only provides services such as customer acquisition, operation and partial collection. Taking into account the actual needs of the capital and the actual situation of the business, the current capital-light operation model also undertakes part of the risk control function, but it cannot be regarded as heavy assets.
Based on this, in the "DuPont Analysis of the Whole Picture of Loan Business Analysis from the Perspective of Analysis", the author associates the scale of the light capital business with the heavy capital, only from the perspective of the new account, the scale of the heavy capital and the light capital is similar to the two ends of the "seesaw", and the rise of one side is inevitably accompanied by the decline of the other party (in reality, it will not be so absolute), it is not difficult to see that only in terms of the light capital model under the mainstream of the industry - credit business income, in addition to the pricing (the pricing is generally 2%-4% of the customer transaction or 20% of the actual interest fee- 25%), which is also related to the conversion efficiency of heavy capital.
Commercial value-added income: that is, non-credit business income, such as common membership income, insurance agency commission income, points mall income, loan superconduction income, equity card sales revenue, credit card recommendation income, etc., from the nature of the business income, this kind of business income is more like a supplement to credit business income, or from the traffic side, more like helping lenders to actively seek the possibility of realizing every traffic purchased by their real money.
The growth of this revenue is not only affected by factors such as business operation models, pricing, and business strategies, but also by the scale and profitability of the credit business of the lending platform itself. Therefore, it is also a key business of supervision and public opinion monitoring.
Operating costs include various costs in the business process, including operating (promotion) costs, credit investigation costs, payment costs, post-loan costs, customer service costs, etc., in addition to the customer acquisition costs mentioned above.
- The operating cost is mainly related to the promotion of activation, and the level of the cost depends on the way of promoting the activation and the scale of users; The cost of credit investigation is mainly related to the credit granting process, and the cost depends on the amount of incoming pieces and the pricing of various external parameters called by the risk control model.
- The cost of payment is mainly related to repayment, and the cost depends on the size of the transaction (there are also fees per transaction) and the rate of the third-party payment channel.
- The cost of post-loan is strongly related to overdue, and the cost depends on the overdue rate, the recovery rate and the unit labor cost.
- The cost of customer service is strongly related to the size of the transaction (balance in credit) and the internal transaction structure, and the cost depends on the scale and labor structure.
In terms of nature, the operating costs other than customer acquisition are similar to the "stepped semi-fixed costs" often referred to in financial management, that is, they change with the scale but remain unchanged within a certain scale range. From the perspective of the development of the industry, with the intensification of competition and the investment of new technologies such as AI, such costs show a downward trend.
In addition to the risk cost of the lending platform, in addition to the level of risk control management, customer group structure and channel structure, the change of external macro factors is also an important influencing parameter, such as the impact of the previous epidemic, the overdue loan platform is generally higher, and there are still some platforms whose bad debt rate has not recovered to the pre-epidemic level.
Other costs, including labor costs, management expenses, R&D expenditures and other costs in the middle and back office of the loan platform, are basically fixed costs and are not too strongly correlated with the business scale.
So far, with the help of the concept of "DuPont analysis system", we have completed the dismantling of the underlying business logic of the loan platform, and have a simple understanding of each business driver.
Below, we will summarize the core nodes of loan assistance on the basis of decomposition, and extract the "business flywheel" of the industry.
02
With the DuPont Analytics System
Build a flywheel for the loan industry
The purpose of dismantling is to explore the underlying business logic of each business, and to find the answer to solve the business dilemma.
Based on the DuPont analysis method and the understanding of the loan business, the author tries to deduce the cycle diagram of the business structure module of the loan platform. This is shown in the figure below.
The first module is customer acquisition. Customer acquisition is the starting point, and the core task of customer acquisition is to find "living water" and accumulate "effective users", so as to provide a user base for the platform to expand the scale of transactions, so the focus of the assessment of the customer acquisition department is "how to attract new users with high quality".
The second module is management. The purpose of business is to promote activity, whether it is credit promotion, first loan promotion, re-lending promotion, value-added promotion...... Its essence is to maintain the activity of users using the platform products, and only active users can contribute transactions and ultimately convert them into revenue and profits of the platform, so the core indicator of the assessment of the business department is "operating profit after excluding risk costs".
The third module is risk. Risk is the "life and death line" of the platform, and the risk cost accounts for 40%-50% of the revenue (equivalent to cannibalizing 30%-40% of the net profit), and the core of the risk management department is how to balance the two ends of the scale of pricing and risk, and achieve a reasonable level of risk under the premise of controlling risk, and maximize scale and profits.
The fourth module is profitability. This module can also be called the result of the first three modules, the purpose of for-profit organizations is to make profits, and any strategy or business behavior that is not for profit is not sustainable.
"Customer acquisition, operation, risk control, and profit" constitute the operating flywheel of the loan platform, and it is also the "diagnostic" for the loan platform to measure and test the health of its business. That is to say, as an institutional manager, you can judge the operation of the institution through changes in key indicators such as customer acquisition, operation, risk control, and profitability; In turn, by adjusting these key link indicators, business adjustments can be made; From a strategic point of view, taking the helm of this business flywheel can also achieve strategy formulation and landing.
03
epilogue
It can be seen that the DuPont analysis system has a relatively clear context and implementation steps in the financial analysis and strategy formulation of the loan industry.
We hope that this approach will be adopted and promoted by the industry. Of course, there are still some problems to be solved in the application of DuPont analysis system in the loan industry, I hope this article will play a role in throwing bricks and stones, if you are interested in this, I also hope that peers will put forward more opinions and suggestions, leave a message in the comment area below this article to discuss, and work together for the healthy development of the loan industry.
In the next article, we will use the methodology described in this article to introduce leading platforms such as Qifu Technology, Xinye Technology, and Lexin as cases to explain in detail the hidden business logic under their performance statements.