Credit concession through credit scoring: Analysis and application proposal

• Main Authors: Oriol Amat, Raffaele Manini, Marcos Antón Renart
• Format: Article
• Language: Catalan
• Published: OmniaScience 2017-01-01
• Series: Intangible Capital
• Subjects: credit scoring, banking, default
• Online Access: http://www.intangiblecapital.org/index.php/ic/article/view/903

Purpose: The study herein develops and tests a credit scoring model which can help financial institutions in assessing credit requests.  Design/methodology/approach: The empirical study has the objective of answering two questions: (1) Which ratios better discriminate the companies based on their being solvent or insolvent? and (2) What is the relative importance of these ratios? To do this, several statistical techniques with a multifactorial focus have been used (Multivariate Analysis of Variance, Linear Discriminant Analysis, Logit and Probit Models). Several samples of companies have been used in order to obtain and to test the model.  Findings: Through the application of several statistical techniques, the credit scoring model has been proved to be effective in discriminating between good and bad creditors.  Research limitations:  This study focuses on manufacturing, commercial and services companies of all sizes in Spain; Therefore, the conclusions may differ for other geographical locations. Practical implications:  Because credit is one of the main drivers of growth, a solid credit scoring model can help financial institutions assessing to whom to grant credit and to whom not to grant credit. Social implications: Because of the growing importance of credit for our society and the fear of granting it due to the latest financial turmoil, a solid credit scoring model can strengthen the trust toward the financial institutions assessment’s.  Originality/value: There is already a stream of literature related to credit scoring. However, this paper focuses on Spanish firms and proves the results of our model based on real data. The application of the model to detect the probability of default in loans is original.