Credit concession through credit scoring: Analysis and application proposal

Abstract

Purpose: The study herein develops and tests a credit scoring model which can help financial institutions in assessing credit requests. Design/methodology: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/implications:This study focuses on manufacturing, commercial and services companies of all sizes in Spain; Therefore, the conclusions may differ for other geographical locations.-51-Intangible Capital -http://dx.doi.org/10.3926/ic.903 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 deny it. 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.

Credit Scoring for the Supermarket and Retailing Industry: Analysis and Application Proposal

“…The five methodologies behaved well in predicting bankruptcy, in particular, one year before the event took place; moreover, it contrasts to how it was possible to predict up to five years in advance the bankruptcy with a level of accuracy superior to 78% and how none of these methodologies resulted to be superior in this classification. This agrees with the previous results where it was posed as a sample how bankruptcy prediction models have a predictive capacity of up to five years before a company goes bankrupt and how it was expected that the closer we get to the bankruptcy event the higher the predictive ability, so the values of the ratios deteriorate at a higher intensity (Marín, Antón and Mondragón, 2011), (Amat, Antón and Manini, 2016). The rest of this section will briefly describe some of the more interesting academic works related to credits scoring.…”

“…As Amat, Antón and Manini (2016) this study has the aim of identifying a function which discriminates the companies based on their ability of being solvent. Companies with higher probability to meet their debt obligations will be considered solvent whereas companies yielding lower a probability of meeting their debt obligations will be considered insolvent.…”

“…To perform the analysis, we use data coming from the SABI database focusing on companies operating in the following industries: Table 1) reflects the aim of the authors to follow the Altman approach and to continue the work started by Amat, Antón and Manini (2016). (2016) The time interval has been chosen taking into account the proximity to the last financial crises, so the effects it should have had on the Z score of the companies, but avoiding the big impact the crisis itself might have generated.…”

“…However, credit scoring techniques, might be influenced by industry specific characteristics as well as the institution applying it. This paper builds on the work of Amat, Antón and Manini (2016) which addresses the limitation of industry specificity when applying a Z score model for credit scoring purposes. In fact, their model once tested on real companies' data, had extremely high overall results in assessing the solvency of specific companies including the television industry, the airplane industry and much more (Amat, Antón and Manini, 2016).…”

“…This paper builds on the work of Amat, Antón and Manini (2016) which addresses the limitation of industry specificity when applying a Z score model for credit scoring purposes. In fact, their model once tested on real companies' data, had extremely high overall results in assessing the solvency of specific companies including the television industry, the airplane industry and much more (Amat, Antón and Manini, 2016). However, this model could not be properly applied to both financial institutions and supermarkets/retailing industries because of their industry specificity.…”