| Abstract [eng] |
After the bank gives a loan, the risk of not pursuing the liabilities comes, so it needs to be reduced. One way to manage this risk is debtor credibility evaluation. Research object: debtor credibility evaluation. The objektive of this paper is to analyze debtor credibility evaluation and evaluate enterprise “Fausta” evaluation using debtor credibility evaluation models. Debtor credibility evaluation model is made from debtor financial rates and their intake methods. The most analyzed intake methods in science literature are linear discriminant analysis, logit, probit methods; the least used: neural networks, decision trees. In this work debtor credibility evaluation models are analysed, they are classified according to two criterions: qualitative models (descriptive) and quantitative models (estimated). Both classification models can betters evaluation debtor credibility. The following models are used to evaluate enterprise “Fausta” credibility: qualitative models: 6C ir SAPACTRPS and quantitative analysis models: Tafler (1974), Springate (1978), Lis (1999), Shumway (2001), Altman Z modelis (1968), Ohlson (1980), Fulmer (1984). Research of enterprise “Fausta” shows, that bank can give a loan. Qualitative analysis models give qualitative information about debtor’s experience, skills, and purpose of the loan. Quantitative models give more information about financial rates. Biggest disandvantage of qualitative analysis models is human factor. Disandvantage of quantitative models is a weight system, which averages the least values. The work consists of 55 pages (without literature’s source list, abbreviations list and additions), 12 tables and 26 pictures. |
