A hybrid algorithm of improved case-based reasoning and multi-attribute decision making in fuzzy environment for investment loan evaluation

This paper presents a model to support the banking managerial decisions in the evaluation of investment plans, especially on rejecting inappropriate plans that can be done in short time (less than hour) and with minimal cost. Because there are some uncertainties in the evaluation process, our proposed model utilises fuzzy set theory to define the problem space in which an acceptance or rejection decision for a submitted investment plan is made. The model is based on lessons-learned concept and developed through the combination of case-based reasoning (CBR) and multiple attribute decision making in fuzzy environment. The model uses an enhanced version of CBR in which a novel concept as solution's truth value is implemented. A set of investment plans is evaluated to show the applicability and efficiency of the model. Different scenarios in terms of sensitivity analysis are also mentioned to capture managerial insights. Comparing the obtained results of the model with those of other algorithms shows its better proximity to human reasoning and decision making.

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