Machine Learning
Neural network credit scoring models
Computers and Operations Research - Neural networks in business
Credit rating analysis with support vector machines and neural networks: a market comparative study
Decision Support Systems - Special issue: Data mining for financial decision making
Application of support vector machines to corporate credit rating prediction
Expert Systems with Applications: An International Journal
Credit scoring with a data mining approach based on support vector machines
Expert Systems with Applications: An International Journal
Credit risk assessment with a multistage neural network ensemble learning approach
Expert Systems with Applications: An International Journal
Support vector machines for credit scoring and discovery of significant features
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
A new fuzzy support vector machine to evaluate credit risk
IEEE Transactions on Fuzzy Systems
| Hi-index | 12.05 |
This paper compares support vector machine (SVM) based credit-scoring models built using Broad (less than 90days past due) and Narrow (greater than 90days past due) default definitions. When contrasting these two types of models, it was shown that models built using a Broad definition of default can outperform models developed using a Narrow default definition. In addition, this paper sought to create accurate credit-scoring models for a Barbados based credit union. Here, the results of empirical testing reveal that credit risk evaluation at the Barbados based institution can be improved if quantitative credit risk models are used as opposed to the current judgmental approach.