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With respect to different confidence on evaluation result from different decision-makers to the hybrid multi-attribute with risk, an approach to group decision- making based on prospect theory and projection theory is proposed. Firstly, a tuple is established to record the evaluation result and the hybrid decision information. Then the element of the tuple is changed into a single triangular fuzzy number by the transformation rule. Considering the confidence degree from single decision-maker to evaluation information, the group decision information is aggregated and the weights of attributes are calculated based on the intuitionistic fuzzy set theory. The improved projection method and prospect theory are proposed to rank the alternatives respectively. Finally, an application case is given to demonstrate the effectiveness and feasibility of the proposed approach.

Most studies have concentrated on building an accurate credit scoring model to decide whether or not to grant credit to new applicants and the efforts to build more accurate credit scoring model seems to be not significant. In this paper, we proposed a hybrid data mining technique which contains two processing stages. In the clustering stage, the samples of the accepted and new applicants are grouped into homogeneous clusters, the isolated samples are deleted and inconsistent samples are relabeled. In the classification stage, support vector machines used samples with new labels to build the scoring model. The difference from the other credit scoring model is that the samples were classified into three or four classes, rather than two the good and the bad credit classes. Experimental results based on the credit data set provided by a local bank in China showed that by choosing a proper cut-off point, super classification accuracy of the good and the bad credit is obtained. Risk management strategies are developed according to the characteristic of each class.

Reject inference is a term that distinguishes attempts to correct models in view of the characteristics of rejected applicants. The main difficulty in establishing reject inference model is that the i®through-the-door' applicant population is unavailable. In this paper, we propose a hybrid data mining technique for reject inference. It is a three-stage approach: k-means cluster, support vector machines classification and computation of feature importance. By combining the samples of the accepted applicants and the new applicants, we obtain representative samples. To some extent, this is cost-free. Analytic results demonstrate that our method improves the predictive performance while still retaining interpretability.