IJSTR

International Journal of Scientific & Technology Research

Home Contact Us
ARCHIVES
ISSN 2277-8616











 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

IJSTR >> Volume 8 - Issue 10, October 2019 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Predicting And Managing Credit Risk By Implementing Scorecard Using Hybrid Strategy With Trust Rating

[Full Text]

 

AUTHOR(S)

M.S.Irfan Ahmed, P.Ramila Rajaleximi

 

KEYWORDS

Dual Scoring Model, Application Scoring, Behavioural Scoring, Credit Bureau Scoring, Hybrid Scoring Strategy, Sequential-Matrix Model, Trust Rating.

 

ABSTRACT

Financial institutions possess a great deal of credit risk in assessing credit application for approval. In recent days, to assess, manage and to make decisions on the credit risks of the customers, financial institutions employ internal scorecards. However, major banks use several existing one-dimensional credit scoring model which may lead to inaccurate assessment results. In this paper, a three-dimensional hybrid credit scoring technique has been proposed that includes sequential application scoring along with the dual credit scoring matrix model. Dual credit scoring model uses behavioural credit scoring and credit bureau scoring for computing the trust rating. Also, the behavioural scoring model employs an optimized multiple rank score based feature selection for accurate scoring. On employing the signed approach based trust ratings, the customers are categorized into three risk groups for assessing and managing the customer credit risks. The credit strategies to be followed in making decisions are also presented along with the empirical analysis. The results from the analysis show that the proposed method provides 88% precision with 43.17 K-S statistics value.

 

REFERENCES

[1] J. Lohokare, R. Dani, and S. Sontakke, “Automated data collection for credit score calculation based on financial transactions and social media,” In International Conference on Emerging Trends & Innovation in ICT (ICEI), IEEE, pp. 134-138, 2017, doi: 10.1109/ETIICT.2017.7977024.
[2] Paisabazaar.com, “How to Build or Maintain a Healthy CIBIL Score,” available online at https://www.paisabazaar.com/cibil/9-tips-build-maintain-healthy-cibil-score/
[3] CIBIL, Part of TransUnion, “Loan Approval Process,” available online at https://www.cibil.com/loan-approval-process
[4] K. Nurlybayeva, G. Balakayeva, “Algorithmic scoring models,” Applied Mathematical Sciences, vol. 7, no. 12, pp. 571-586, 2013.
[5] M. Ala'raj, and M.F. Abbod, “A new hybrid ensemble credit scoring model based on classifiers consensus system approach,” Expert Systems with Applications, vol. 64, pp. 36-55, 2016.
[6] C.L. Chuang and R.H. Lin, “Constructing a reassigning credit scoring model,” Expert Systems with Applications, vol. 36, no. 2, pp. 1685-1694, 2009.
[7] C.F. Tsai, and J.W. Wu, “Using neural network ensembles for bankruptcy prediction and credit scoring,” Expert systems with applications, vol. 34, no. 4, pp .2639-2649, 2008.
[8] A. Khashman, “Neural networks for credit risk evaluation: Investigation of different neural models and learning schemes,” Expert Systems with Applications, vol. 37, no. 9, pp. 6233-6239, 2010.
[9] N. Metawa, M.K. Hassan, and M. Elhoseny, “Genetic algorithm based model for optimizing bank lending decisions,” Expert Systems with Applications, vol. 80, pp. 75-82, 2017.
[10] G. Wang, J. Ma, L. Huang, and K. Xu, “Two credit scoring models based on dual strategy ensemble trees,” Knowledge-Based Systems, vol. 26, pp. 61-68, 2012.
[11] B.W. Yap, S.H. Ong, and N.H.M. Husain, “Using data mining to improve assessment of creditworthiness via credit scoring models,” Expert Systems with Applications, vol. 38, no. 10, pp. 13274-13283, 2011.
[12] B.W Chi, and C.C. Hsu, “A hybrid approach to integrate genetic algorithm into dual scoring model in enhancing the performance of credit scoring model,” Expert Systems with Applications, vol. 39, no. 3, pp.2650-2661, 2012.
[13] G. Dong, K.K. Lai, and J. Yen, “Credit scorecard based on logistic regression with random coefficients,” Procedia Computer Science, vol. 1, no. 1, pp. 2463-2468, 2010.
[14] A.C. Bahnsen, D. Aouada, and B. Ottersten, “Example-dependent cost-sensitive logistic regression for credit scoring,” In 2014 13th International Conference on Machine Learning and Applications, IEEE, pp. 263-269, 2014.
[15] C. Bolton, “Logistic regression and its application in credit scoring,” Doctoral dissertation, University of Pretoria, 2009.
[16] S.Y. Sohn, D.H. Kim, and J.H. Yoon, “Technology credit scoring model with fuzzy logistic regression,” Applied Soft Computing, vol. 43, pp. 150-158, 2016.
[17] C. Bravo, L.C. Thomas, and R. Weber, “Improving credit scoring by differentiating defaulter behavior,” Journal of the operational research society, vol. 66, no. 5, pp. 771-781, 2015.
[18] M. Alborzi, and M. Khanbabaei, “Using data mining and neural networks techniques to propose a new hybrid customer behaviour analysis and credit scoring model in banking services based on a developed RFM analysis method,” International Journal of Business Information Systems, vol. 23, no. 1, pp. 1-22, 2016.
[19] B.W. Chi, C.C. Hsu, and M.H. Ho, “Enhancing credit scoring model performance by a hybrid scoring matrix,” African Journal of Business Management, vol. 7, no. 18, pp. 1791-1805, 2013.
[20] K. Thiagarajan, A. Raghunathan, G. Ponnamal Natarajan, G. Poonkuzhali, and Prashant Ranjan, “Weighted Graph Approach for Trust Reputation Managements,” International Conference on Intelligent Systems and Technologies, Published in Proc. Of World Academy of Science and Technology, vol. 56, pp. 830-836, 2009.
[21] G. Poonkuzhali, K. Sarukesi, G.V. Uma, “Web content outlier mining through mathematical approach and trust rating,” in the book titled Recent Researches in Applied Computer and Applied Computational Science, Included in ISI/SCI Web of Science and Web of Knowledge, Venice, Italy, pp. 77-82, 2011.
[22] L.C. Thomas, J. Ho, and W.T. Scherer, “Time will tell: behavioural scoring and the dynamics of consumer credit assessment,” IMA Journal of Management Mathematics, vol. 12, no. 1, pp. 89-103, 2001.
[23] S. Kotsiantis, B.D. Kanellopoulos, and P. Pintelas, “Data Preprocessing for Supervised Leaning,” International Journal of Computer Science, vol. 2, no. 2, pp. 111-117, 2006.
[24] P. Ramila Rajaleximi, M.S. Irfan Ahmed, and Ahmed Alenezi,, “Feature Selection using Optimized Multiple Rank Score Model for Credit Scoring,” International Journal of Intelligent Engineering and Systems, vol. 12, no.2 pp. 74-84, 2019.
[25] E.K. Laitinen, and T. Laitinen, “Bankruptcy prediction: Application of the Taylor’s expansion in logistic regression,” International Review of Financial Analysis, vol. 9, no. 4, 327–349, 2000.
[26] G. Zeng, “A necessary condition for a good binning algorithm in credit scoring,” Applied Mathematical Sciences, vol. 8, no. 65, pp. 3229-3242, 2014.
[27] N. Siddiqi, Credit Risk Scorecards. Developing and Implementing Intelligent Credit Scoring. John Wiley & Sons, Inc, 2006.
[28] E. Kalapanidas, N. Avouris, M. Craciun, and D. Neagu, “Machine Learning Algorithms: A Study on Noise Sensitivity,” In Proc. of Balcan Conference in Informatics, pp. 356-365, 2003
[29] R.J. Urbanowicz, M. Meeker, W. La Cava, R.S. Olson, and J.H. Moore, “Relief-based Feature Selection: Introduction and Review,” Journal of Biomedical Informatics, vol. 85, pp. 189-2013, 2018.