Serov, Vasiliy2013-05-202013-05-202011-0470046http://bspace.buid.ac.ae/handle/1234/101DISSERTATION WITH DISTINCTIONWith credit decisions being defined as increasingly important and crucial decisions in today’s business environment, affecting entire life-span of a business, failure results in high costs for firms, society and economy in general. We, thus, see evaluation of business failure as emerged scientific field seeking optimal prediction models, depending on specific characteristics of the firms studied. The main job of a credit analyst is to determine risk relative to the portfolio one is analyzing. In such analysis quantitative factors are vital variables as they allow an expert to say whether a company with a certain, say, leverage ratio, is risky or not. Credit analysts of public large corporates are availed with sophisticated tools of measuring associated credit risk of their borrowers, which allows them to efficiently summarize substantial size of the analytical data, like financials, into standardized condensed number, probability or rating, that would rank a corporate effectively along the ladder of associated default probability and allow analyst to focus his expertise more productively and apply the subjective, expertise-driven, analysis to produce a verdict where it is needed the most. Middle market lending, however, is still primarily a subjective process. No universal and objective benchmarks are existent up-to-date that could be applied across all Small and Medium Enterprises (SMEs) effectively and would allow their loans to be securitized. As market value information is valuable and not reflected in financials, private firms default models are sub-optimal to those companies with traded equity (Falkenstein, Boral, & Carty, RiskCalc(TM) For Private Companies: Moody's Default Model, 2000). After many years of research, there is no universally accepted model for predicting probability of default (PD) for SMEs, based on causal specification of underlying economic factors. This contributes significantly to the financing gap that the SME sector is faced with. Lack of credit to this sector persists heavily despite the fact that SMEs are a major contributor to economy’s output and development. Importance of making an accurate judgment about counterparty’s PD is high especially for financial institutions, like banks, whose margins on net cash flow are so narrow and their leverage is so high that small differences in actual and estimated assets’ quality may affect their solvency substantially and, hence, solvency of financial sector of economy as a whole. The lack of precise methods in measuring of credit risk results into numerous problems for lenders: (i) high unexpected losses, (ii) high cost of credit applications review, (iii) frequently credit decision-making is separated from collection function, while the feedback from the latter is vital for development of judgmental skills in people who approve credit applications, (iv) lack of experienced personnel and due to lack of specific methods, personnel is hard to train. Each loan that is mispriced or mistakenly granted represents a lost opportunity. Hence, importance of better credit analysis should not be underestimated. In light of the above, this study tries to focus on specifics of credit risk analysis of SMEs and discuss how the approach should be modified to incorporate these unique features of smaller companies.encredit risk assessmentSmall and Medium Enterprises (SMEs)Dissertation