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.