Abstract
The literature has documented a positive relationship between the use of credit scoring for small business loans and small business credit availability, broadly defined. However, this literature is hampered by the fact that all of the studies are based on a single 1998 survey of the very largest U.S. banking organizations. This paper addresses a number of deficiencies in the extant literature by employing data from a new survey of the use of credit scoring in small business lending, primarily by community banks. The survey evidence suggests that the use of credit scores in small business lending by community banks is surprisingly widespread. Moreover, the scores employed tend to be the consumer credit scores of the small business owners, rather than the more encompassing small business credit scores that include data on the firms as well as on the owners. Our empirical analysis suggests that credit scoring is associated with an initial increase in small business lending activity that moderates over time and no change in the quality of the loan portfolio. Supplementary analysis suggests that the use of credit scores for small business lending has a negative initial effect on community bank profitability that moderates over time.
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Notes
These findings are also consistent with small business lending at greater distances by large banks found by other researchers without access to data on which lending technologies the banks use (e.g., Petersen and Rajan 2002; Hannan 2003; Brevoort 2006; Brevoort and Hannan 2006). However, the increased distances in these studies may also reflect the use of other transactions technologies that do not require close contact with the firm. Nonetheless, one recent study finds that increases in distance are greater for large banks using SBCS (DeYoung et al. forthcoming).
See Frame et al. (2001) for detailed information about the original SBCS survey.
Additional survey findings that are not surprising are: (1) in most cases community banks purchase scores externally, rather than using internal models, and (2) community banks generally do not use the scores to make automated decisions regarding acceptance/rejection of the loan applications.
Non-response bias is a natural concern whenever one is working with survey data with a fairly low 22 percent response rate. To examine this issue, we conducted difference-in-means tests across the four stratification variables for responders and non-responders. We could not reject the null hypothesis that the means were the same, suggesting that non-response bias is not a significant issue.
A “commitment to small business lending” was measured across two variables: (1) the ratio of loans secured by non-farm, nonresidential properties to total assets, and (2) the ratio of C&I loans to total assets. From these ratios, categorical variables were created (C1 and C2). Each took a value of one if the ratio was less than the median, a value of two if the ratio was between the median and the third quartile, a value of three if the ratio was between the third quartile and the 95th percentile, and a value of four if the ratio was greater than the 95th percentile. The sample strata (S) were then based on joint membership in categories C1 and C2 using the rule that S = min[C1, C2].
In practice, all auto decision banks allow for some judgmental overrides, and all supplementing banks use some rules for automatic rejections.
Prior to 2001, there were three different Call Reports for banks with domestic offices only: banks with fewer than $100 million in total assets; banks with between $100 million and $300 million in total assets; and those with more than $300 million in total assets. For the first two categories, loan performance information for “commercial and industrial loans” was combined with that for “all other loans.”
The original amount of a loan is the maximum of the loan amount and the amount of the line of credit or commitment, if any. For loan participations and syndications, the original amount refers to the entire amount of credit originated by the lead lender.
Fifteen banks reported using credit scores prior to June 1993. We treat these observations as censored and hence measure YEARS SINCE as of that date. Thus, for these institutions, YEARS SINCE = 1 in 1994 and =2 in 1995 and so forth through the sample period.
All banks that report using credit scoring for loans between $50,000 and $100,000 also report using the technology for loans under $50,000.
We use deposit markets (and weighting) instead of loan markets because deposits is the only variable on which we have bank branch location.
This is Call Report item RCON 6999.
Note that while 21% of the observations are associated with credit scoring over the 1993–2005 period, 44% of the institutions report using credit scores at the end of the sample.
As a robustness check, we also study loans up to $250,000. We again find the positive and statistically significant initial effect of using credit scores, although the passage of time is statistically insignificant.
In additional analysis, we find that the ratio of total expenses to gross total assets is positively related to the introduction of credit scoring with the effect moderating over time.
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The views expressed do not necessarily reflect those of the Federal Reserve Bank of Atlanta or its staff. The authors thank Beth Kiser for providing the banking market data and Pam Frisbee for research assistance. Valuable comments have been provided by an anonymous referee, Charles Cowan, Bill Keeton, Margaret Miller, Nathan Miller, George Pennacchi, Wako Watanabe, John Wolken, and seminar and conference participants at the Federal Reserve Bank of Kansas City, World Bank, and Financial Management Association Meetings.
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Berger, A.N., Cowan, A.M. & Frame, W.S. The Surprising Use of Credit Scoring in Small Business Lending by Community Banks and the Attendant Effects on Credit Availability, Risk, and Profitability. J Financ Serv Res 39, 1–17 (2011). https://doi.org/10.1007/s10693-010-0088-1
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DOI: https://doi.org/10.1007/s10693-010-0088-1