An analysis on business intelligence models to improve business performance

Business intelligence is an effective technology to take right decisions at right time for the survival of any business. Business intelligence can be applied to all kind decision making and prediction analysis. Business performance can be identified by using bankruptcy prediction. In this research we are developing a business intelligence model to predict the business performance by using bankruptcy prediction as well as we are finding important features to improve the prediction accuracy of bankruptcy model. The proposed BI model applies both Quantitative and Qualitative factors to predict bankruptcy. Quantitative factors are measured from financial variables and Qualitative factors are measured from non-financial variables using data mining techniques. To identify the important features from the quantitative bankruptcy models this research work applies Real Genetic Algorithm. The Real Genetic Algorithm analysis the non linear relation between financial variables in Fulmer bankruptcy model and identifies important features. The experimental result shows that accuracy level of original threshold value α and generated threshold value β is more than 90%.