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Although the study of credit ratings has focused on traditional credit bureau resources, scholars have recently emphasized the importance of big data. The purpose of this paper is to examine both how these data affect the credit evaluations of small businesses and how financial managers use them to stabilize their risks.

Using data from 97,889 data points for normal guarantees and 1,678 data points for accidents in public funds, the authors explore the effects of trade area grades as well as the superiority of the use of big data when evaluating credit ratings for small businesses.

The results indicate that the grade information of trade areas is useful in predicting accident rates, particularly for small businesses with high credit scores (AAA-A). On the other hand, the accident rates of small businesses with low credit scores increased from 3.15-16.67 to 3.20-33.3 percent. These findings demonstrate that accident rates for the businesses with high credit scores decrease, but accident rates for businesses with low credit scores increase when using the grades of trade areas.

The authors contribute to the literature in two ways. First, this study provides one of the first investigations on information on trade areas through public financial perspectives, thereby extending the financial risk and retail literature. Second, the current study extends the research on the credit evaluation of small businesses through the big data application of real transaction-based trade areas, answering the call of Park et al. (2012), who recommended an exploration of the relationship between business start-ups and financial risk.