Modeling hard and soft facts for SMEs: Some international evidence

Abstract

This paper asks how well the use of quantitative and qualitative variables can improve the assessment of companies' creditworthiness and how this result can be influenced by the economic and financial peculiarities of countries. We harden qualitative variable measures to model soft information aimed at scoring microfirms, small, and medium‐sized firms. The structural survey covers Germany, Italy, and the UK in a sample of about 17 thousand companies observed during the financial crisis. Soft facts are determined within the balanced scorecard framework in order to find out the impact of customers, business processes, learning and growth, and financial perspectives. Our findings show that credit models integrating soft variables optimize the risk estimation, but estimates are country‐specific and should be tailored to the characteristics of each economic system.

Digitalisation promotes adoption of soft information in SME credit evaluation: the case of Indian banks

“…A participant notes that the hardened soft information could be easily transmitted within the bank and increased the accuracy of the internal rating of small- and medium-sized enterprises (Godbillon-Camus & Godlewski, 2005 ). One participant highlights using a traditional rating-based approach for financing SMEs that integrates soft information and addresses its constraints (Matthias et al, 2019 ).…”

“…Based on extant literature, "networks or alliances/partnerships" and "business and leadership" factors reduce lender losses (Yosano & Nakaoka, 2019 ). It should be noted that Matthias et al ( 2019 ) argue that credit risk management cannot be generalised across countries, especially when soft and hard information is combined (Matthias et al, 2019 ). There are four commonly perceived soft information attributes within these factors: supplier relationship, customer relationship, business plan, and managerial succession.…”

Controlling risks to ensure financial stability and reducing volatility

“…While the first article is focused on the relationship between rating and financial structure of listed companies and therefore generally on larger firms, the second article focuses specifically on credit risk in small‐ and medium‐sized enterprises (SMEs). In particular, the objective of Matthias, Giammarino, and Gabbi () (“Modelling Hard and Soft Facts for SMEs. Some International Evidence”) is to show the contribution of soft variables to the estimation of the probability of default in three important European countries: Germany, Italy, and the United Kingdom.…”