The week's pick
Random Articles
Reseach Article
Credit Scoring using Machine Learning Techniques
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 161 - Number 11 |
| Year of Publication: 2017 |
| Authors: Sunil Bhatia, Pratik Sharma, Rohit Burman, Santosh Hazari, Rupali Hande |
10.5120/ijca2017912893
|
Sunil Bhatia, Pratik Sharma, Rohit Burman, Santosh Hazari, Rupali Hande . Credit Scoring using Machine Learning Techniques. International Journal of Computer Applications. 161, 11 ( Mar 2017), 1-4. DOI=10.5120/ijca2017912893
Abstract
Lenders such as banks and credit card companies while reviewing a client’s request for loan use credit scores. Credit scores help measure the creditworthiness of the client using a numerical score. Now it has been found out that the problem can be optimized by using various statistical models. In this study a wide range of statistical methods in machine learning have been applied, though the datasets available to the public is limited due to confidentiality concerns. Problems particular to the context of credit scoring are examined and the statistical methods are reviewed.
References
- [Li, Xiao-Lin, and Yu Zhong. "An overview of personal credit scoring: techniques and future work." (2012).
- West, Jarrod, and Maumita Bhattacharya. "Some Experimental Issues in Financial Fraud Mining." Procedia Computer Science 80 (2016): 1734-1744.
- Yap, Bee Wah, Seng Huat Ong, and Nor Huselina Mohamed Husain. "Using data mining to improve assessment of creditworthiness via credit scoring models." Expert Systems with Applications 38.10 (2011): 13274-13283.
- Jayagopal, B. "Applying Data Mining Techniques to Credit Hand, David J., and William E. Henley. "Statistical classification methods in consumer credit scoring: a review." Journal of the Royal Statistical Society: Series A (Statistics in Society) 160.3 (1997): 523-541.
- James, Gareth, et al. An introduction to statistical learning. Vol. 6. New York: springer, 2013.
- Kennedy, Kenneth. "Credit Scoring Using Machine Learning." (2013).
- Khandani, Amir E., Adlar J. Kim, and Andrew W. Lo. "Consumer credit-risk models via machine-learning algorithms." Journal of Banking & Finance34.11 (2010): 2767-2787.
- Yap, Bee Wah, Seng Huat Ong, and Nor Huselina Mohamed Husain. "Using data mining to improve assessment of creditworthiness via credit scoring models." Expert Systems with Applications 38.10 (2011): 13274-13283.
- Siddiqi, Naeem. Credit risk scorecards: developing and implementing intelligent credit scoring. Vol. 3. John Wiley & Sons, 2012.
- Hand, David J., and William E. Henley. "Statistical classification methods in consumer credit scoring: a review." Journal of the Royal Statistical Society: Series A (Statistics in Society) 160.3 (1997): 523-541.
- Chen, Tianqi, and Tong He. "xgboost: eXtreme Gradient Boosting." R package version 0.4-2 (2015).
- Breiman, Leo. "Random forests." Machine learning 45.1 (2001): 5-32.
- Izenman, Alan Julian. "Linear discriminant analysis." Modern Multivariate Statistical Techniques. Springer New York, 2013. 237-280.
- Hosmer, David W., and Stanley Lemeshow. Multiple logistic regression. John Wiley & Sons, Inc., 2000.
- Tianqi Chen and Carlos Guestrin. “XGBoost: A Scalable Tree Boosting System.”(2016).
Index Terms
Keywords