Fuzzy Modeling of Minimal Crediting Risks in Investment Decisions

This article proposes a novel fuzzy technology to support the investment decisions. While choosing among competitive investment projects, this technology provides the selection of projects with minimal crediting risks, makes ranking of chosen projects and then allows to optimally allocate investment amounts between several of them. The technology combines two fuzzy-statistical methods and solution of bicriteria discrete optimization problem, providing three stages of investment projects’ evaluation. At the first stage preliminary selection of projects with minor risks is made on the basis of the Expertons Method [5, 6]. The second stage makes ranking of chosen projects using the modified Possibilistic Discrimination Analysis Method. The latter is a further modification of the Possibilistic Discrimination Analysis Method [20], carried out by the authors. These two stages represent a new approach of credit scoring. The third stage of technology leads to the most profitable investments into several of the previously selected projects by applying the method developed by the authors for discrete possibilistic bicriteria problems [16, 21]. The article provides an example of the investment decision-making that explains the work of the offered technology. Key-Words: Investment project risks, expert estimates, positive and negative measures of discrimination, experton, possibility distribution, bicriteria discrete optimization problem.