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At present, the world is accelerating its digital transformation, and data assets are increasingly becoming an important strategic resource to promote and accelerate the development of the digital economy. Digital elements and digital technologies are reconstructing new business formats and new models. At the same time, the construction machinery industry's downward cycle bottoming process continues, domestic demand support remains weak, and market adjustment pressure continues. Due to the decline of the industry market, equipment buyers' insufficient equipment rental rate, falling rents, falling repayment rates and other factors have weakened the first source of repayment, making it difficult for other repayment sources to supplement, increasing the credit risk of construction machinery companies.

Credit sales business is essentially about taking risks to obtain income, repayments, and profits from selling equipment. The core is credit risk management. The origin of credit risk is information asymmetry. Against the background of the wave of digitalization and the downturn of the construction machinery industry, the traditional credit risk management model of construction machinery is both a big challenge and a big opportunity. How to build data-driven risk control capabilities and risk control systems to penetrate the constraints of information asymmetry is worthy of in-depth consideration and practice by every construction machinery company.

01. Information asymmetry and credit risk

According to the theory of information economics, the micro-foundation of credit risk is information asymmetry. Information asymmetry refers to differences in the acquisition, processing and use of information between the two parties in the market. The party with sufficient information is in a more advantageous position, while the party with poor information is in a relatively disadvantaged position. Due to the professionalism, concealment and search costs of information, both parties to the transaction often possess asymmetric information. Different information channels and the amount of information will cause different risks and benefits for both parties to the transaction. The party at a disadvantage in the amount of information There will be greater risks.

Information asymmetry is divided into ex-ante information asymmetry and ex-post information asymmetry according to the different time points when the transaction contract is signed. In the case of ex-ante information asymmetry, the party with the information advantage hides the information, and in the case of ex-post information asymmetry, the party possesses the information. The dominant side hides its actions. Information asymmetry occurs before the transaction contract is signed, causing "adverse selection" problems; it occurs after the transaction contract is signed, causing "moral hazard" and "principal-agent" problems.

For construction machinery companies, it is impossible to fully, real-timely and accurately grasp the credit and repayment ability information of the equipment purchaser before the transaction, such as assets and liabilities, external performance, operating capabilities, etc.; during the contract performance process, the company's operations, Equipment operations, revenue collection, debt solvency, etc. are also difficult to monitor. Before and after the transaction, the equipment buyer takes advantage of its own information advantages and hides key information based solely on maximizing personal interests. This causes problems such as adverse selection and moral hazard, and forms credit risks.

02. Design and existing problems of traditional information asymmetry solution mechanism

1. Signal display mechanism to solve adverse selection

Market signal display means that in order to solve the problem of adverse selection, the party with information advantage displays its own high-quality information and sends market signals to those with information disadvantage in some way to enhance the confidence of the other party. Through the signal display mechanism, effective high-quality information can be used to form credit risk identification of equipment buyers.

The purchasers of construction machinery are generally small, medium and micro enterprises, which provide limited asset information, and it is difficult to provide relatively complete and authentic financial statement data. In the credit review process, there are many missing key credit information, which affects the effectiveness of the market signal display mechanism. play.

2. Signal screening mechanism to solve adverse selection 

Market signal screening means that before conducting market transactions, the party with inferior information first uses relevant contracts and mechanism design to allow the party with superior information to send out signals showing certain characteristics of itself, so as to facilitate the judgment of the party with inferior information, thereby changing its information disadvantage in market transactions. the behavior of. Construction machinery companies can design contract terms that associate down payment ratios with price discounts. Customers with high down payment ratios will enjoy higher price discounts, thus distinguishing equipment buyers with different credit levels.

For construction machinery companies, the difficulty in using this mechanism lies in risk pricing. The corresponding prices for different credit ratings and the application of business conditions require reasonable calculation and quantitative pricing.

3. Risk sharing and benefit sharing mechanism to solve the problem of moral hazard 

For moral hazard, the mechanism design is generally a reasonable incentive mechanism, risk sharing and benefit sharing, thereby solving the principal-agent problem through endogenous motivation. Construction machinery companies can stipulate breach of contract clauses in the contract, such as liquidated damages. Equipment buyers with a history of default records will face the consequences of default such as increased prices and financing interest rates when repurchasing; equipment buyers who pay in advance will enjoy interest exemptions and exemptions. A certain price and interest rate discount will be given when the contract is fulfilled and repurchased.

This mechanism can play a certain role in guiding equipment buyers to perform their contracts, but there is great uncertainty in business operations and equipment operations. If default risks cannot be predicted and warned in advance, corresponding risk control measures will be passive and lagging.

03. Data-driven credit risk control

The uncertainty of credit risk lies in the uncertainty of whether it occurs, when it occurs, scope of impact, direction of impact, duration, and degree of impact. In order to mitigate credit risks caused by information asymmetry, construction machinery companies must design digital toolboxes based on the theoretical mechanism of economics to promote the transformation of credit risk management from empirical analysis to data analysis and forward-looking prediction, and realize risk identification-risk quantification-risk Efficient closed-loop management and control of assessment-risk monitoring-risk reporting.

1. Risk identification. The first step in risk identification requires a good data foundation. Construction machinery companies generally have their own information systems such as CRM, ERP, and DMS. However, master data management (MDM) has inconsistent management rules, missing data fields related to credit management, and information is not dynamically updated. Some companies even have offline Table management and basic data quality cannot meet the mining and analysis requirements, and comprehensive data governance is required.

Construction machinery companies use data governance to improve the quality of equipment purchasers' internal transactions and credit data. After system development, they integrate and apply external compliant third-party data to form complete internal and external transaction and contract performance data, thereby in the credit review stage. , identify potential credit risks through system credit scanning and database comparison, form a 360-degree risk view of the buyer, restore and depict the three major reports of assets and liabilities, cash flow, and profit, and serve as subsequent credit enhancement measures and credit rating An important reference basis for grading to solve the problem of information asymmetry before transactions.

2. Risk quantification. With the data foundation in place, the second step is to build a suitable quantitative analysis model. Based on the historical performance or default data of a large sample of enterprises, the influencing factor indicators are selected to construct a default probability measurement and analysis model including explained variables, explanatory variables, and control variables. In addition to quantitative indicators, some indicators that are difficult to quantify, such as gender, age, region, years of employment, etc., can be converted into dummy variables, and parameters are estimated based on the Logistic credit risk assessment model.

By controlling variables and adding explanatory variables to regression analysis, the parameter values ​​are estimated. According to whether the parameter value is significant, it is judged whether the explanatory variable has an impact on the default rate. After the parameter value is significant, the positive or negative value of the parameter value is used to determine the positive or negative impact on the default rate. According to the elastic coefficient value of the parameter value, the degree of impact on the default rate can be judged. Based on the regression analysis results, the indicators that affect the default rate are incorporated into the credit review and rating model, and the model is continuously optimized and iterated.

3. Risk assessment. With the quantitative foundation in place, the third step is to construct an estimation model for the expected loss (EL) of construction machinery companies. EL is the loss that may be suffered due to the equipment buyer's default within a certain period, and includes three elements, namely probability of default (PD), loss given default (LGD) and risk exposure (EAD), EL = PD × LGD × EAD. 

PD and LGD can be calculated using logistic regression and chi-square test measurement analysis methods, combined with credit history, repayment ability, income level, debt situation and other data. Among them, PD can also construct a credit rating scale based on historical default situations and model estimates, in which credit ratings and default probabilities form a mapping relationship. For example, five types of credit ratings can be formed: A, B, C, D, and E. It can also be used for certain types of credit. The grades are further subdivided (Class A can be divided into AAA, AA, and A), and different credit grades correspond to different default probabilities. At the same time, the buyer's credit rating will be adjusted in a timely manner based on changes in internal and external transactions, contract performance data, and credit changes discovered during due diligence.

Through model calculation, we can play the core role of risk pricing and realize that expected income can cover expected losses. For equipment buyers with different risk levels, different down payment ratios, guarantee amount ratios, sales prices, financing interest rates, and credit lines can be formulated to achieve differentiated pricing and balance risks and returns. Buyers with high expected losses will control the quota, charge corresponding risk premiums, and strictly formulate approval rules to deal with the uncertainty of future credit changes with the certainty of the rules.

4. Risk monitoring. Traditional risk monitoring mainly monitors whether the buyer's repayment is overdue, as well as changes in the overdue amount and duration. With the development of industrial Internet of Things technology and its full integration with mobile Internet technology, risk monitoring integrates "human" and "thing" data to form a new risk identification perspective. 

Through Internet of Things monitoring, "three matches" for risk judgment are formed. First, based on the equipment operating rate and operating hours data, advance analysis and early warning of the equipment purchaser's ability to pay in recent periods are judged whether the equipment usage income is consistent with the repayment amount. Matching; second, based on equipment operating rate and operating hours, combined with changes in market rents and repayments, to determine whether the monthly payment amount of the equipment purchaser matches its project income; third, based on Internet of Things data and market price trends, determine whether the residual value of the equipment Match the amount to be paid for the device. At the same time, the asset security of the equipment is monitored based on the equipment operation trajectory.

5. Risk reporting. The construction machinery industry is a cyclical industry that is greatly affected by the macroeconomic environment, fixed asset investment, etc. Changes in credit risk in the construction machinery industry must be calculated and analyzed based on the micro-assessment model and combined with macroeconomic indicators to form a risk report. When market conditions change dramatically and abnormally, a stress test model must be established to conduct stress tests on credit sales assets to determine whether the assets have sufficient resilience to cope with sudden changes in the market. 

Under the data-driven credit risk management model, construction machinery companies must form an organic connection between risk control operations and data. Through intelligent, multi-dimensional identification and analysis of risk data, they can form a risk panorama and risk map, and at the same time report on corporate risk management goals. Achievability, completeness of risk management organization system construction, effectiveness of major risk response strategies, and risk assessment in the dimensions of enterprise, product line, region, customer group, etc. 

"I look for spring all day long but can't see it. My pawn shoes break through the clouds on the mountaintop. When I come back, I smell the plum blossoms. Spring is already on the branches." In the digital age, "spring is already on the branches". Construction machinery companies must accelerate the digital transformation of risk management, transform from organization-driven to data-driven, promote digital elements to reshape credit risk management models, and establish data decision-making and action mechanisms to ensure certainty. A data-driven risk control system to cope with the uncertainty of future risks.

Source: Construction Machinery Today