The Value of Non-Financial Information in SME Risk Management

By Gabriele Sabato

Edward Altman

By Edward Altman

By Nick Wilson

Abstract

Within the commercial client segment, small business lending is gradually becoming a major target for many banks. The new Basel Capital Accord has helped the financial sector to recognize small and medium sized enterprises (SMEs) as a client, distinct from the large corporate. Some argue that this client base should be treated like retail clients from a risk management point of view in order to lower capital requirements and realize efficiency and profitability gains. In this context, it is increasingly important to develop appropriate risk models for this large and potentially even larger portion of bank assets. So far, none of the few studies that have focused on developing credit risk models specifically for SMEs have included qualitative information as predictors of the company credit worthiness. For the first time, in this study we have available non-financial and ‘event’ data to supplement the limited accounting data which are often available for non-listed firms. We employ a sample consisting of over 5.8 million sets of accounts of unlisted firms of which over 66,000 failed during the period 2000-2007. We find that qualitative data relating to such variables as legal action by creditors to recover unpaid debts, company filing histories, comprehensive audit report/opinion data and firm specific characteristics make a significant contribution to increasing the default prediction power of risk models built specifically for SMEs.
Within the commercial client segment, small business lending is gradually becoming a major target for many banks.

I. Introduction

The Basel Capital Accord and the recent financial crisis have provided renewed impetus for lenders to research and develop adequate default/failure prediction models for all of the corporate and retail sectors of their lending portfolios. The Basel II definition of financial distress, 90 days overdue on credit agreement payments, is the operational definition for major lenders. The literature on the modeling of credit risk for large, listed companies is extensive and gravitates between two approaches (1) the z-score approach of using historical accounting data to predict insolvency (e.g. Altman 1968) and (2) models which rely on securities market information (Merton, 1974). In retail lending, risk modeling can be undertaken using very large samples of high frequency consumer data and combinations of in-house portfolio data (e.g. payment history) and bureau data from the credit reference agencies to develop proprietary models.In the past, retail lending was mainly synonymous with consumer lending.

More recently, following the introduction of Basel II, an increasing number of banks have started to reclassify commercial clients from the corporate area into the retail one. Although this decision may have been originally motivated by expected capital savings (see Altman and Sabato (2005)), financial institutions have soon realized that the major benefits were on the efficiency and profitability side. Banks are also realizing that small and medium sized companies are a distinct kind of client with specific needs and peculiarities that require risk management tools and methodologies specifically developed for them (see Altman and Sabato (2007)).

Indeed, small and medium sized enterprises are the predominant type of business in all OECD economies and typically account for two thirds of all employment. In the UK, unlisted firms make up the majority of firms which ultimately fail. Of the 1.2 million active companies that are registered, less than 12,000 are listed on the stock market. In the US, private companies contribute over 50% of GDP (Shanker and Astrachan, 2004). The flow of finance to this sector is much researched as it is seen as crucial to economic growth and success but, from the lending perspective, research on credit risk management for small companies is relatively scarce.

The most likely way to ensure a flow of finance to SMEs is by improving information and developing adequate risk models for this sector. Techniques for modeling corporate insolvency have long been applied as a means of assessing and quantifying the risk of listed companies and the research into failure rate prediction has focused almost exclusively on listed companies. Much of the pioneering work on bankruptcy prediction has been undertaken by Altman (1968) and (1993). These earlier works were undertaken primarily during the 1960s, although extensions of this work to developing countries appeared during the 1990s (see Altman and Narayanan (1997)). Early work into corporate failure prediction involved determining which accounting ratios best predict failure, employing primarily multiple discriminant analysis or logit/probit models.

In most of these accounting ratio based studies, ratios are calculated at a pre-determined time before bankruptcy (usually one year) and as such these models are often referred to as static models. Exceptionally, these studies focus on the use of data other than accounting data, for example von Stein and Ziegler (1984) examine the impact of managerial behaviour on failure. Invariably, this earlier work suffers from only a small sample of failed firms being available for analysis.Recently, Altman and Sabato (2007) apply, with some success, a distress prediction model estimated specifically for the US SME sector based on a set of financial ratios derived from accounting data. They demonstrate that banks should not only apply different procedures (in the application and behavioral process) to manage SMEs compared to large corporate firms, but these organizations should also use scoring and rating systems specifically addressed to the SME portfolio. The lack of any non-financial and compliance information about the companies in their sample presents a significant limit forcing them to exclude a relevant portion of small companies without accounting data.

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