标题： 人工智能技术在银行客户偿债能力预测中的应用：决策树（dt）类型cart的应用案例 显示英文标题

标题： The application of techniques derived from artificial intelligence to the prediction of the solvency of bank customers: case of the application of the cart type decision tree (dt)

摘要： 在本研究中，我们将从人工智能技术派生出的CART型决策树（DT-CART）方法应用于银行客户偿债能力的预测，为此我们使用了银行客户的的历史数据。 然而我们采用了数据挖掘技术的流程，为此我们首先进行了数据预处理，在此过程中我们清理了数据，并删除了所有具有异常值或缺失值的行以及列为空的行，然后确定了要解释的变量（因变量或目标变量），我们也考虑通过单变量分析以及相关矩阵来消除所有不显著的解释变量（自变量），然后我们使用SPSS工具应用了我们的CART决策树方法。 在完成我们的模型构建过程（AD-CART）后，我们开始了评估和测试我们的模型性能的过程，通过此过程我们发现我们的模型的准确率和精确度为71%，因此我们计算了误差比例，发现误差率为29%，这使我们得出结论，我们的模型在精确度、可预测性方面处于相当好的水平，特别是在预测我们的银行客户偿债能力方面非常精确。 显示英文摘要

摘要： In this study we applied the CART-type Decision Tree (DT-CART) method derived from artificial intelligence technique to the prediction of the solvency of bank customers, for this we used historical data of bank customers. However we have adopted the process of Data Mining techniques, for this purpose we started with a data preprocessing in which we clean the data and we deleted all rows with outliers or missing values as well as rows with empty columns, then we fixed the variable to be explained (dependent or Target) and we also thought to eliminate all explanatory (independent) variables that are not significant using univariate analysis as well as the correlation matrix, then we applied our CART decision tree method using the SPSS tool. After completing our process of building our model (AD-CART), we started the process of evaluating and testing the performance of our model, by which we found that the accuracy and precision of our model is 71%, so we calculated the error ratios, and we found that the error rate equal to 29%, this allowed us to conclude that our model at a fairly good level in terms of precision, predictability and very precisely in predicting the solvency of our banking customers.