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Automobile consumer credit is extremely popular in developed countries like those in Europe and North America. Specifically, credit plays a significant role in the development of passenger cars while also generating significant profits for financial institutions that offer such credit products. With the rapid development of China’s economy, automobiles have gradually become an important form of durable goods in everyday life. In recent years, consumer demand for automobile credit has increased, with many consumers preferring to purchase a new car on credit.
However, the Chinese automobile consumer credit industry is still in its infancy, with an evolving management system and no personal credit system. As a result, both commercial banks and financial companies that issue car credit face sizeable default risk. Inadequate research of traditional automobile finance companies’ risk controls has created many loopholes in the control mechanisms of automobile loans.
Another risk for financing companies is the excessive cost of commissions paid to retail stores. It should be noted that inadequate commissions may lead salespeople to reduce their efforts. As such, determining the optimal commission has become a risk-reducing action for vehicle financing companies. Accordingly, academics and professionals seek to construct an effective evaluation system based on historical data and customer behavior to minimize default risks and develop a balanced commission strategy in China.
In terms of existing risks and problems associated with automobile consumer credit default, this study provides theoretical guidance and empirical evidence for automobile financing companies to guide them in designing a balanced commission structure and identifying customers who will pay back the loan on time. The research helps companies identify desirable customers, determine the optimal amount of commission to be paid, and maintain a sustainable business. The present study answers the following two research questions:
By leveraging economic and financial theories of consumer credit risk control, the authors first established a logistic model to predict the probability of a “good” customer (i.e., an individual who will repay on time, payoff, or payoff early) based on both macro- and micro-level factors. Abnormal execution status is defined under two circumstances. First, the borrower does not repay the loan on time under the grace period specified by the terms. Second, civil proceedings have been brought to court. The macro-level factors include the sale price, loan amount, interest rate, and contract length. Meanwhile, the individual level variables include age, marital status, education, and home ownership. Additionally, the authors constructed a quadratic regression model to determine the optimal commission structure that balances profitability with incentivizing the retail store staff.
This study compiled 129,858 transactions carried out by a Chinese consumer automobile credit company. The results show that the macro-level variables are negatively associated with the probability of good behavior, whereas the personal level variables exhibit a positive association. A negative coefficient for the commission squared in the quadratic profit equation indicates that the relationship between the profit and commission forms an inverted “U” shape. This provides a theoretical foundation for the optimal commission amount for maximum profit. The result of the study enables 4S (sales, spare parts, service, and survey) stores in undeveloped, developing, or developed countries to address issues related to credit risk. Relevant issues include finding factors connected with credit risk and determining an optimal commission amount for maximum profit. Further contribution will be explored in the discussion and conclusion sections.
The article is organized as follows. Section two summarizes the related works. Section three elaborates on the research questions, outlines the data collection process, and proposes empirical models to explore the research question. The model estimations, analysis, and discussions are presented in section four. Two extended models to incorporate monthly data and keywords search frequency are investigated in section five. Finally, section six concludes with the results and limitations of the present study and future research directions.