Dear Mohammad,

Please go through the following papers for your understanding of the trend:

1. Bose, I., Kan, C., Tsz, C., Ki, L., & Hung, W. (2007). Data Mining for Credit Scoring. Advances in Banking Technology and Management: Impacts of ICT and CRM: Impacts of ICT and CRM.

2. Huang, C. L., Chen, M. C., & Wang, C. J. (2007). Credit scoring with a data mining approach based on support vector machines. Expert systems with applications, 33(4), 847-856.

3. Thomas, L., Crook, J., & Edelman, D. (2017). Credit scoring and its applications (Vol. 2). Siam.

4. Bhatia, S., Sharma, P., Burman, R., Hazari, S., & Hande, R. (2017). Credit Scoring using Machine Learning Techniques. International Journal of Computer Applications, 161(11), 1.

5. Garrido, F., Verbeke, W., & Bravo, C. (2018). A Robust profit measure for binary classification model evaluation. Expert Systems with Applications, 92, 154-160.

6. Thomas, L. C. (2000). A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers. International journal of forecasting, 16(2), 149-172.

Thanks,

Sobhan

Dear Mohammad,

Please go through the following papers for your understanding of the trend:

1. Bose, I., Kan, C., Tsz, C., Ki, L., & Hung, W. (2007). Data Mining for Credit Scoring. Advances in Banking Technology and Management: Impacts of ICT and CRM: Impacts of ICT and CRM.

2. Huang, C. L., Chen, M. C., & Wang, C. J. (2007). Credit scoring with a data mining approach based on support vector machines. Expert systems with applications, 33(4), 847-856.

3. Thomas, L., Crook, J., & Edelman, D. (2017). Credit scoring and its applications (Vol. 2). Siam.

4. Bhatia, S., Sharma, P., Burman, R., Hazari, S., & Hande, R. (2017). Credit Scoring using Machine Learning Techniques. International Journal of Computer Applications, 161(11), 1.

5. Garrido, F., Verbeke, W., & Bravo, C. (2018). A Robust profit measure for binary classification model evaluation. Expert Systems with Applications, 92, 154-160.

6. Thomas, L. C. (2000). A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers. International journal of forecasting, 16(2), 149-172.

Thanks,

Sobhan

Machine Learning Techniques and deep learning are changing the credit scoring method.

1. Li, Zhiyong, et al. "Reject inference in credit scoring using Semi-supervised Support Vector Machines." Expert Systems with Applications 74 (2017): 105-114.

2. Maldonado, Sebastián, Juan Pérez, and Cristián Bravo. "Cost-based feature selection for Support Vector Machines: An application in credit scoring." European Journal of Operational Research 261.2 (2017): 656-665.

3. Dumitrescu, Elena, et al. "Machine Learning for Credit Scoring: Improving Logistic Regression with Non Linear Decision Tree Effects." (2018).

4. Luo, Cuicui, Desheng Wu, and Dexiang Wu. "A deep learning approach for credit scoring using credit default swaps." Engineering Applications of Artificial Intelligence 65 (2017): 465-470.

5. Bequé, Artem, and Stefan Lessmann. "Extreme learning machines for credit scoring: An empirical evaluation." Expert Systems with Applications 86 (2017): 42-53.

6. Mishra, Chandrahas, and D. L. Gupta. "Deep Machine Learning and Neural Networks: An Overview." IAES International Journal of Artificial Intelligence 6.2 (2017): 66.

Thanks for your valuable helps.

Hybrid models with meta-heuristics are useful tools also.