Risk and Reward Analysis Model

Abstract: This paper presents a comprehensive review of the studies conducted in the application of data mining techniques focus on credit scoring and market segmentation. The studies are categorized and classified into enterprise, individual and small and midsized (SME) companies credit scoring. Data mining techniques are also categorized to single classifier, Hybrid methods and Ensembles. Variable selection methods are also investigated separately because there is a major issue in a credit scoring problem. The findings of this literature review reveals that data mining techniques are mostly applied to an individual credit score and there is inadequate research on enterprise and SME credit scoring. Data mining uses sophisticated software to collect, analyze, and report on large accumulations of data in order to extract information that provides insight into business and consumer processes. Customized data mining software is designed to convert raw data into useful information. Among the beneficial strategies for data mining is customer segmentation. Customer segmentation can be defined as the process of dividing a customer base into distinct and internally consistent groups in effort to develop and differentiate marketing strategies according to customer characteristics.

Keywords: Liquidity analysis, Profitability analysis,, Trend Analysis, Fund Flow. Cash Flow, Financial Risk, Country Risk, Moratorium Period, Disbursement Period.