Abstract
Internal credit scoring and rating play an essential role for bank in credit risk management and in pricing loans as well as assigning appropriate lending policy for each class of customers; and also for determining the level of regulatory capital reserve. This implies the importance of deeply understanding about the internal rating models and the respective approaches in execution for bank risk managers.
In 2005, the State Bank of Vietnam promulgated Decision 493/2005/QĐ-NHNN and later Decision 18/2007/QĐ-NHNN in 2007 about debt classification, determining provision and reserve. In addition, the Decisions required commercial banks to establish an internal credit rating system that aligned with Basel Accord II of Basel Committee and Banking Supervision. In 2006, Bank for Development and Investment Vietnam (BIDV) officially adopted the new Internal Credit Rating System (ICRS), which is approved by the State Bank of Vietnam, and was consulted by Ernst & Young Audit firm. The IRCS includes 54 criteria (10 financial and 40 non-financial criteria) to asses a firm creditability.
The ICRS is widely applied for the whole BIDV system including the head quarter and all level of BIDV branches. Credit analysts from different BIDV branches may have different point of view in assessing non-financial (qualitative) criteria and thus lead to different credit rating for the same company in the system. To disregard the difference between branches, this thesis is conducted using data that BIDV consider best practiced credit score rated by BIDV’s credit analyst, whose competence are equivalent and whose point of view mostly aligned with BIDV strategy. The research aims to investigate the most important ICRS criteria by using artificial learning machine method, specifically, the Artificial Neural Network. The researches primarily focused on analyzing the ICRS applied for economic organizations with the historical data collected from 33 companies from 14 industries in the year 2015. After a deliberate research process, the research has evaluated several constrains of the outstanding model aiming to constructively contribute for improvement in the future rating activities at BIDV.
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References
Abdou, H., Pointon, J., El-Masry, A.: Neural nets versus conventional techniques in credit scoring in Egyptian banking. Expert Syst. Appl. 35(3), 1275–1292 (2008)
Angelini, E., Tollo, G.D., Roli, A.: A neural network approach for credit risk evaluation. Q. Rev. Econ. Finan. 48(4), 733–755 (2008)
Atiya, A.F.: Bankruptcy prediction for credit risk using neural networks: a survey and new results. IEEE Trans. Neural Netw. 12(6), 929–935 (2011)
Baesens, B., Van Gestel, T., Viaene, S., Stepanova, M., Suykens, J., Vanthienen, J.: Benchmarking state-of-the-art classification algorithms for credit scoring. J. Oper. Res. Soc. 54(6), 627–635 (2003)
Boyacioglu, A.M., Kara, Y., Baykan, K.O.: Predicting bank financial failures using neural networks, support vector machines and multivariate statistical methods: a comparative analysis in the sample of Savings Deposit Insurance Fund (SDIF) transferred banks in Turkey. Expert Syst. Appl. 36(2), 3355–3366 (2009)
Cherubini, U., Lunga, G.D.: Liquidity and credit risk. J. Finan. 61(5), 79–95 (2014)
Desai, V., Crook, J., Overstreet, G.: Credit scoring models in the credit union environment using neural networks and genetic algorithms. IMA J. Math. Appl. Bus. Ind. 8(4), 232–256 (1997)
Dutta, S., Shekhar, S.: Bond rating: a non-conservative application of neural networks. In: IEEE International Conference on Neural Networks, San Diego, 24–27 July 1988, vol. 2, pp. 443–450. IEEE (1988)
Fethi, D.M., Pasiouras, F.: Assessing bank efficiency and performance with operational research and artificial intelligence techniques: a survey. Eur. J. Oper. Res. 204(2), 189–198 (2010)
Ghatge, A., Halkarnikar, P.: Ensemble neural network strategy for predicting credit default evaluation. Int. J. Eng. Innovative Technol. (IJEIT) 202(207), 223–225 (2013)
Huang, Z., Chen, H., Hsu, C.-J., Chen, W.-H., Wu, S.: Credit rating analysis with support vector machines and neural networks: a market comparative study. Decis. Support Syst. 37(4), 543–558 (2004)
Khemakhem, S., Boujelbene, Y.: Credit risk prediction: a comparative study between discriminant analysis and the neural network approach. Account. Manag. Inf. Syst. 14(1), 60–78 (2015)
Lee, Y.C.: Application of support vector machines to corporate credit rating prediction. Expert Syst. Appl. 33(1), 67–74 (2007)
Lopez, J.A., Saidenberg, M.R.: Evaluating credit risk models. J. Bank. Finan. 24(1–2), 151–165 (2000)
Ong, C.-S., Huang, J.-J., Tzeng, G.-H.: Building credit scoring models using genetic programming. Expert Syst. Appl. 29, 41–47 (2005)
Singleton, J.C., Surkan, A.J.: Modeling the judgment of bond rating agencies. In: Midwest Finance Association Annual Meeting, 28–31 March, Chicago, IL (1990)
West, D.: Neural network credit scoring models. Comput. Oper. Res. 27(11–12), 1131–1152 (2000)
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Hai, P.Q., Ngoc, T.T.L., Do Thanh Phuong, B. (2018). An Alternate Internal Credit Rating System for Construction and Timber Industries Using Artificial Neural Network. In: Anh, L., Dong, L., Kreinovich, V., Thach, N. (eds) Econometrics for Financial Applications. ECONVN 2018. Studies in Computational Intelligence, vol 760. Springer, Cham. https://doi.org/10.1007/978-3-319-73150-6_59
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