Abstract: The process evaluation of default risk is to evaluate the default risk of small enterprises in the process of borrowing and considering the increase of evaluation characteristics. The existing default risk evaluation mostly considers the static evaluation (dynamic evaluation) of the same evaluation characteristics at a certain time (a certain time series). In the big data environment, the features used for evaluation are growing explosively. It is unrealistic and infeasible for banks to rebuild the evaluation model for each additional feature. The process evaluation of pedagogy is introduced into the default risk evaluation for the first time. Considering the multi-dimensional data background, from the perspective of adding new criteria and new indicators, four ways are proposed to construct the default risk evaluation structure in the credit process: one is to reconstruct the default risk evaluation model every time an index or a criterion layer is added; the second is to construct the evaluation model independently for each criterion layer, and take the evaluation result of each criterion evaluation model as the input variable to construct the default risk evaluation model; the third is to construct the default risk evaluation model by taking the latest evaluation result and all indicators in the newly added as the input variable; the fourth method is to build a default risk evaluation model by taking" the latest k evaluation results and all indicators in the newly added as input variables. Taking the actual loan data of small enterprises of a commercial bank in China since 1994 as the object, an empirical study is made by establishing a neural network model. The research shows that there is no significant difference in the discrimination accuracy of the default risk evaluation model constructed by the above four methods. Meanwhile, mode 2 has higher discrimination accuracy, which can dynamically update the evaluation model in time under the background of big data, and save operation time and complexity.

Key words: multidimensional data; default risk; process evaluation; small enterprises