Information and Management
Neural network credit scoring models
Computers and Operations Research - Neural networks in business
A methodology to explain neural network classification
Neural Networks
Using neural network ensembles for bankruptcy prediction and credit scoring
Expert Systems with Applications: An International Journal
Computational Statistics & Data Analysis
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Bankruptcy prediction for credit risk using neural networks: A survey and new results
IEEE Transactions on Neural Networks
Exploring the behaviour of base classifiers in credit scoring ensembles
Expert Systems with Applications: An International Journal
Two-level classifier ensembles for credit risk assessment
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Credit scoring model is an important tool for assessing risks in financial industry, consequently the majority of financial institutions actively develops credit scoring model on the credit approval assessment of new customers and the credit risk management of existing customers. Nonetheless, most past researches used the one-dimensional credit scoring model to measure customer risk. In this study, we select important variables by genetic algorithm (GA) to combine the bank's internal behavioral scoring model with the external credit bureau scoring model to construct the dual scoring model for credit risk management of mortgage accounts. It undergoes more accurate risk judgment and segmentation to further discover the parts which are required to be enhanced in management or control from mortgage portfolio. The results show that the predictive ability of the dual scoring model outperforms both one-dimensional behavioral scoring model and credit bureau scoring model. Moreover, this study proposes credit strategies such as on-lending retaining and collection actions for corresponding customers in order to contribute benefits to the practice of banking credit.