Differentiated use of small business credit scoring by relationship lenders and transactional lenders: Evidence from firm–bank matched data in Japan

Abstract

This paper examines the ex-post performance of small and medium-sized enterprises (SMEs) that obtained small business credit scoring (SBCS) loans. Using a unique Japanese firm-bank matched dataset, we identify whether an SME has obtained an SBCS loan and, if so, from which type of bank: a relationship lender or a transactional lender. We find that the ex-post probability of default after the SBCS loan was provided significantly increased for SMEs that obtained an SBCS loan from a transactional lender. We also find that the lending attitude of relationship lenders in the midst of the recent global financial crisis became much more severe if a transactional lender had extended an SBCS loan to a firm. These findings suggest that SBCS loans by a transactional lender are detrimental to a relationship lender's incentive to monitor SMEs and maintain relationships. In contrast, we do not find such detrimental effects for SBCS loans extended by a relationship lender.

Is Relationship Lending Still a Mixed Blessing? A Review of Advantages and Disadvantages for Lenders and Borrowers

“…Ono et al . () finds that relationship lending reduces default probabilities for Japanese firms. The authors evaluate the ex post probability that a borrower might default on a loan granted based on a credit scoring and whether this probability differs when the loan is granted by a relationship lender (the primary bank) or other institutions.…”

“…Cotugno et al (2013b) find that thanks to relationship lending Italian banks were able to maintain a good quality of their loan portfolio in terms of lower borrower default rates. Ono et al (2014) finds that relationship lending reduces default probabilities for Japanese firms. The authors evaluate the ex post probability that a borrower might default on a loan granted based on a credit scoring and whether this probability differs when the loan is granted by a relationship lender (the primary bank) or other institutions.…”

Topological applications of multilayer perceptrons and support vector machines in financial decision support systems

“…Because the first category of approaches belongs to the family of statistical classifiers, Beaver (1966) explored the financial decision employing 14 financial indicators along with the 158 example set of healthy and bankrupt firms. In a comparable layer, numerous studies have investigated the utility of quadratic discriminant analysis (DA) by Sueyoshi (2006), Cinca and Nieto (2013) on least square DA, Ono, Hasumi, and Hirata (2014) on linear regression models, and Hernandez and Torero (2014) on probit analysis. Altman operated a paired instance design being incorporated 33 pairs of built-up companies that were forecasted upon the size and industrial classification.…”

“…By itself, these classical learners cannot efficiently capture the non-linear connections among the trained features, which is central to the performance of FDSSs modelling. In a comparable layer, numerous studies have investigated the utility of quadratic discriminant analysis (DA) by Sueyoshi (2006), Cinca and Nieto (2013) on least square DA, Ono, Hasumi, and Hirata (2014) on linear regression models, and Hernandez and Torero (2014) on probit analysis. However, these classifiers continue to suffer from the identical hitches that plague LDA, for example, linearity, normality, and independence among predictor, which are ubiquitous in financial databases.…”

Predicting Creditworthiness in Retail Banking with Limited Scoring Data

“…The capability of statistical credit scoring systems to improve decision-making and time efficiencies in the financial sector has widely attracted researchers and practitioners particularly in recent years (see for example, [4,37,[43][44][45]49,51,53,54] ). Credit scoring systems are now regarded as virtually indispensible in developed countries.…”