Predicting of Credit Default by SVM and Decision Tree Model Based on Credit Card Data

Abstract

With the global financial crisis and increased credit risk, default forecasting is playing an increasingly important role in every sector of the economy. Currently, there are linear models and machine learning models for predicting credit defaults. In recent years, big data risk control models are superior to traditional bank models in predicting default rates, and can also conduct business quickly and on a large scale. This paper compares the SVM and the decision tree model in the machine learning model based on the credit card loan data set, and finally evaluates the prediction effect between the two models. According to the study, the decision tree model outperforms the SVM in terms of prediction accuracy. The use of big data to conduct machine learning to predict credit conditions enables financial institutions to serve small, medium and micro enterprises that were difficult to cover by traditional finance on a large scale in the past. It is a world-class innovation in finance.

Financial revolution: a systemic analysis of artificial intelligence and machine learning in the banking sector

“…Other authors also develop and compare different models for credit risk assessment: SVM, DTs, AdaBoost, RF, logistic regression, ANN, and XGBoost. Of these, the different models with greater predictive accuracy are obtained by each author: AdaBoost and RF [35], DT [36]- [40]. On the other hand, Putri et al [41] propose to analyze credit risk using SMV.…”

“…Likewise, it has great advantages in improving the risk management practices of the information system and improving banking business performance [43]. It also improves credit risk management [38], [40], [44], [45]. In credit risk analysis, it would be beneficial not only for banking institutions but also for the customer, since it would allow them to be more informed about the risk of not meeting their payments [46], and it also minimizes credit card fraud [45], [47].…”