In the small-and-medium-enterprise (SME) financing market, due to asymmetric information to which those smaller companies fall victims, the market failure frequently occurs. Accordingly, this paper raises two salient questions: one is on the credit discrimination that exists in the Korean SME financing market and the other is on whether the current financing supply is good enough to ensure the sustainable operation and growth of SMEs. Before answering these questions, we overview the practical and theoretical basis concerning the concepts and its categorization of the funding gap for SMEs. We also look into the SME financing policies and the effects they have on both in domestic and international context. With respect to the funding gap concepts, there are mainly two approaches (Cressy, 2002): positive and normative definitions. Positive definition argues that the funding gap is an equilibrium of an imperfect market, in which lending volume is below the level that would emerge in a perfect market. Normative definition argues that it is a market failure, for which appropriate policy response should be an increase in lending volume. However, this concept classification does not make any difference to the conclusion of this paper. We also introduce another categorization of the debt funding gap(Equinox Management Consultants, 2002): the size gap, the risk gap, the flexibility gap, and the knowledge gap. A size gap asserts that, for the small-and-medium sized firms, the demanded amounts of the debt funding are usually too small to be of interest to the institutional lenders such as large banks. A risk gap entails that lenders do not necessarily price loans solely based on potential risks. Rather, they reject loan applications if estimated risk exceeds a pre-specified cut-off rate or particular standards or if the available collateral is not sufficient. A flexibility gap postulates that according to some SME owners, financial institutions do not provide flexible terms and conditions tailored to their specific loan demands. Finally, a knowledge gap claims that the financial institutions do not understand knowledge-based businesses. We analyze the determinants of the turn down ratio of ‘Small & Medium Business Corporation of Korea' loan applications, using the logit model. We use the loan application data which also contains some firm characteristic information including who applies for the ‘Small & Medium Business Corporation of Korea'lending and records of guarantee projects between 1999 and 2011. The logit regression outcomes show that the coefficients of the credit-related variables and most of the discrimination-related control variables are statistically significant. In particular, the shorter firm age leads to the higher rejection rate, but start-up companies have relatively lower rate of rejection. Depending on the model specification, the firm location and the age of CEO have some effects on the result; metropolitan companies and those with younger CEOs show relatively higher turn-down ratios. As for the female CEO dummy variables, we did not find any statistical significance. We argue that these empirical results should not be interpreted as the credit discrimination evidences for the Korean small-and-medium sized firms. Instead, these results could reflect the characteristics of the ‘Small and Medium Business Corporation of Korea' policies. We estimate the size and proportion of the funding gap caused by the company's growth for the Korean SMEs which are mandated to be externally audited. In this paper, we use the model of Canovi and Venturelli (2008), which predict SMEs' latent funding demand on the basis of growth speed. The firm's growth speed is proxied by its sales growth rate, which in turn drives its financial debt demand. The funding gap reflects this difference that exists between the estimated financial debt funding demands and the real supplies of the financial debt. Our methods consider long-term growth driven funding demand while excluding the one-time capital-investment-driven funding demand. In addition, the estimated parameters are very sensitive with the cross-section and the time-series observations. Therefore, we averaged out each individual firm's parameter estimates across the whole periods. We hope that future researches can resolve these limitations of our methods. We divided our pool of samples into 12 groups according to their asset sizes and industry classifications. The results show that about 70% of the companies considered in the sample is estimated to be lack of funds. For these fund-seeking companies, the group median values of the funding gap lie between 5% and 12% of the asset size. And these funding gap ratios are higher for the large asset groups and the electronic and IT (information and technology) industry group than those of the rest. These results seem to suggest that the firm size and the industry are critical factors that require serious consideration when devising a supporting policy for SMEs.