Application of Data Mining Combined with Fuzzy Multicriteria Decision-Making in Credit Risk Assessment from Legal Service Companies

The purpose is to avert the systematic financial risks from the Internet financial bubble and improve the efficiency of legal service companies’ credit risk assessment ability. Firstly, this study analyzes the commonly used classification model, Support Vector Machine (SVM), and linear regression model, Logistic model, and then puts forward the integrated SVM-Logistic + Fuzzy Multicriteria Decision-Making (FMCDM) to evaluate and analyze the credit risk level of listed companies. In the proposed integrated model, the SVM model classifies the data sample from listed companies, and the Logistic model is used for regression analysis on the credit risk assessment. Based on the credit risk indexes and weight uncertain factors of sample companies, FMCDM based on fuzzy set is applied to obtain the evaluation indexes. Then, the Analytic Hierarchy Process (AHP) is used to obtain the weight of key indexes. Finally, the fit analysis is carried out according to the existing risk status of the sample company and the risk status results of the proposed integrated model. The results show that the integrated SVM-Logistic model is complementary and has high intensive evaluation. According to the fitness value obtained by FMCDM, the company's credit risk status can be accurately evaluated, and the intermediate threshold of corporate credit default risk measurement is 0.56152; if Fit is lower than the threshold, the company’s credit is low, and if Fit is higher than the threshold, the company’s credit is high. Therefore, the data mining technology based on integrated SVM-Logistic model + FMCDM has high precision and feasible application in the credit risk assessment from legal service companies. This study creates a new method model for legal service companies in the field of corporate credit risk assessment and can provide references and ideas for corporate credit risk assessment.

A Study on RB-XGBoost Algorithm-Based e-Commerce Credit Risk Assessment Model

The current method’s e-commerce credit risk assessment is prone to poor data balance and low evaluation accuracy. An RB-XGBoost algorithm-based e-commerce credit risk assessment model is proposed in this study. The adaptive random balance (RB) method is used to sample and process the obtained data to improve the balance degree of the data. An assessment index system is constructed based on the processed data. Based on the risk evaluation index system and the XGBoost algorithm, this paper constructed an e-commerce risk assessment model and assessed the e-commerce credit risk using this model. The experimental results show that the proposed method has good data balance, a high kappa coefficient, and a large receiver operating characteristic (ROC) curve area, which can effectively improve e-commerce credit risk assessment accuracy.

A Longitudinal Systematic Review of Credit Risk Assessment and Credit Default Predictors

This study aims to reveal the predictors of individuals’ financial behavior associated with credit default for accurate and reliable credit risk assessment. Within the scope of credit use research, a systematic review of 108 studies was performed. Among the reviewed studies, a fair number have analyzed the determinants of default and delinquency. A remarkable number has examined the factors affecting outstanding and problematic debt levels, and some have investigated the financial behavior in terms of responsibility, debt repayment, and credit misuse. A wide range of socioeconomic, demographic, psychological, situational, and behavioral factors was explored, and their role in predicting the investigated outcome domain at various time-points was analyzed. The main analysis techniques and mix of predictors in papers also differed based on different time periods. While the synthesis of findings revealed some strong and consistent predictors for each outcome variable, mixed results were obtained for some factors. Additionally, a cluster of new practices that includes a wide range of alternative factors to improve prediction accuracies were uncovered. Study findings revealed a paradigm shift regarding the use of non-traditional data sources, especially big data, and novel techniques.