Abstract
Credit Rating is the valuation of the credit worthiness of the borrowing entity, which gives an indication of the borrower’s current credit position and the probability of default. A credit rating model must be very accurate in doing its predictions because critical decisions are made based on the classification that would have been made for the prospective borrower. Different changes occur in the environment that would have been used to come up with the initial model, which might not be applicable to the current sample population and this might have an effect on the prediction accuracy. Changes to the data stream, economic climate, social and cultural environment may cause a drift. Drift shows that there is a change in probability distribution of the concept under study. Population drift is an example of concept drift. Having a static credit rating model will bring challenges in future predictions, hence, there is the need for designing a dynamic credit rating system that caters for the changes that might occur to the initial population sample in order to maintain the prediction accuracy of the model. In this paper, a detailed literature study was conducted exploring recent solution approaches to drift effect in credit rating models. A comprehensive recent solutions is presented in this paper that could be a source of information of interested researchers.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Žliobaitė, I., Pechenizkiy, M., Gama, J.: An overview of concept drift applications. In: Japkowicz, N., Stefanowski, J. (eds.) Big Data Analysis: New Algorithms for a New Society. SBD, vol. 16, pp. 91–114. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-26989-4_4
Pavlidis, N.G., Tasoulis, D.K., Adams, N.M., Hand, D.J.: Adaptive consumer credit classification. J. Oper. Res. Soc. 63(12), 1645–1654 (2012)
Huang, S., Day, M.: A comparative study of data mining techniques for credit scoring in banking. In: IEEE IRI, San Francisco, California, USA, 14–16 August 2013 (2013)
Huang, Z., et al.: Credit rating analysis with support vector machines and neural networks: a market comparative study. Decis. Support Syst. 37(4), 543–558 (2004)
Keramati, A., Yousefi, N.: A proposed classification of data mining techniques in credit scoring. In: Proceedings of the International Conference on Industrial Engineering and Operations Management (2011)
Paleologo, G., Elisseeff, A., Antonini, G.: Subagging for credit scoring models. J. Oper. Res. 201(2), 490–499 (2010)
Li, X.L., Zhong, Y.: An overview of personal credit scoring: techniques and future work. Int. J. Intell. Sci. 2(04), 181 (2012)
Luo, S.T., Cheng, B.W., Hsieh, C.H.: Prediction model building with clustering-launched classification and support vector machines in credit scoring. Expert Syst. Appl. 36(4), 7562–7566 (2009)
Fogarty, D.J.: Using genetic algorithms for credit scoring system maintenance functions. Int. J. Artif. Intell. Appl. 3(6), 1 (2012)
Hand, D.J., Adams, N.M.: Selection bias in credit scorecard evaluation. J. Oper. Res. Soc. 65(3), 408–415 (2014)
Kelly, M.G., Hand, D.J., Adams, N.M.: The impact of changing populations on classifier performance. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 367–371. ACM, August 1999
Kadwe, Y., Suryawanshi, V.: A review on concept drift. IOSR J. Comput. Eng. 17, 20–26 (2015)
Knotek, J., Pereira, W.: Survey on Concept Drift. Faculty of Economics, University of Porto, Portugal. https://is.muni.cz/el/1433/podzim2011/PA164/um/drift_detection_methods.pdf
Sun, Y., Tang, K., Zhu, Z., Yao, X.: Concept drift adaptation by exploiting historical knowledge. IEEE Trans. Neural Netw. Learn. Syst. 29, 4822–4832 (2018)
Zliobaite, I., et al.: Next challenges for adaptive learning systems. ACM SIGKDD Explorations Newsl 14(1), 48–55 (2012)
Krempl, G., Hofer, V.: Classification in presence of drift and latency. In: 2011 IEEE 11th International Conference on Data Mining Workshops (ICDMW), pp. 596–603. IEEE, December 2011
Wei, G., Mingshu, C.: A new dynamic credit scoring model based on clustering ensemble. In: 3rd International Conference on Computer Science and Network Technology (2013)
Adams, N.M., Tasoulis, D.K., Anagnostopoulos, C., Hand, D.J.: Temporally-adaptive linear classification for handling population drift in credit scoring. In: Lechevallier, Y., Saporta, G. (eds.) Proceedings of COMPSTAT’2010, pp. 167–176. Physica-Verlag HD, Heidelberg (2010). https://doi.org/10.1007/978-3-7908-2604-3_15
Nikolaidis, D., Doumpos, M., Zopounidis, C.: Exploring population drift on consumer credit behavioral scoring. In: Grigoroudis, E., Doumpos, M. (eds.) Operational Research in Business and Economics. SPBE, pp. 145–165. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-33003-7_7
Whittaker, J., Whitehead, C., Somers, M.: A dynamic scorecard for monitoring baseline performance with application to tracking a mortgage portfolio. J. Oper. Res. Soc. 58(7), 911–921 (2007)
Romanyuk, K.: A dynamic credit scoring model based on contour subspaces. In: Proceedings of SAI Intelligent Systems Conference (IntelliSys). IntelliSys (2016)
Babu, R., Satish, A.R.: Improved of k-nearest neighbor techniques in credit scoring. Int. J. Dev. Comput. Sci. Technol. 1(2), 1–4 (2013)
Barakat, L., Pavlidis, N., Crone, S.: A context-aware approach for handling concept drift in classification (Doctoral dissertation, Lancaster University) (2018)
Bifet, A., Gama, J., Pechenizkiy, M., Liobait, I.: Handling concept drift: importance challenges & solutions. Tutorial. In: Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (2011)
Baena-Garcia, M., del Campo-Ávila, J., Fidalgo, R., Bifet, A., Gavalda, R., Morales-Bueno, R.: Early drift detection method. In: Fourth International Workshop on Knowledge Discovery from Data Streams, vol. 6, pp. 77–86, September 2006
Klinkenberg, R., Joachims, T.: Detecting concept drift with support vector machines. In: ICML, pp. 487–494, June 2000
Hofer, V., Krempl, G.: Predicting and Monitoring Changes in Scoring Data (2015)
Tsymbal, A.: The problem of concept drift: definitions and related work, vol. 106, no. 2. Computer Science Department, Trinity College Dublin (2004)
Ang, H.H., Gopalkrishnan, V., Zliobaite, I., Pechenizkiy, M., Hoi, S.C.: Predictive handling of asynchronous concept drifts in distributed environments. IEEE Trans. Knowl. Data Eng. 25(10), 2343–2355 (2013)
Zliobaite, I., Bifet, A., Holmes, G., Pfahringer, B.: MOA concept drift active learning strategies for streaming data. In: Proceedings of the Second Workshop on Applications of Pattern Analysis, pp. 48–55, October 2011
Bhatia, S., Sharma, P., Burman, R., Hazari, S., Hande, R.: Credit scoring using machine learning techniques. Int. J. Comput. Appl. 161(11), 1 (2017)
Bifet, A., Holmes, G., Kirkby, R., Pfahringer, B.: MOA: massive online analysis. J. Mach. Learn. Res. 11, 1601–1604 (2010)
Kogeda, O.P., Vumane, N.N.: A model augmenting credit risk management in the banking industry. Int. J. Technol. Diffus. (IJTD) 8(4), 47–66 (2017)
Chikoore, R., Kogeda, O.P.: A credit rating model for Zimbabwe. In: Proceedings of the Southern Africa Telecommunication Networks and Applications Conference (SATNAC), Fancourt, George, Western Cape, South Africa, 4–7 September 2016, pp. 88–89 (2016)
Bhebe, W., Kogeda, O.P.: Shilling attack detection in collaborative recommender systems using a meta learning strategy. In: Proceedings of the IEEE International Conference on Emerging Trends in Networks and Computer Communications, ETNCC 2015, Windhoek Country Club Resort, Namibia, 17–20 May 2015, pp. 56–61 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Chikoore, R., Kogeda, O.P., Ojo, S.O. (2020). Recent Approaches to Drift Effects in Credit Rating Models. In: Zitouni, R., Agueh, M., Houngue, P., Soude, H. (eds) e-Infrastructure and e-Services for Developing Countries. AFRICOMM 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 311. Springer, Cham. https://doi.org/10.1007/978-3-030-41593-8_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-41593-8_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-41592-1
Online ISBN: 978-3-030-41593-8
eBook Packages: Computer ScienceComputer Science (R0)