Abstract
The intensifying need to incorporate knowledge extracted from qualitative information into banks’ lending decision has been recognized in recent times, particularly for micro lenders. In this study, the multi-faceted credit information is captured in an integrated form using XML to facilitate the discovery of knowledge models encompassing a broad range of credit risk related aspects. The quantitative and qualitative credit data obtained from the industry partner describes existing lender profiles. The experiments are performed to discover classification models for the performing or non-performing lenders in one problem setting, and the duration of payment delay in another. The results are compared with a common credit risk prediction setting where qualitative data is excluded. The findings confirm the role of domain experts’ knowledge as well as qualitative information on loan performance assessment, and describe a number of rules indicating refinement of the banks’ lending policy requirement.
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References
Tambunan, T.: SME Development, Economic Growth, and Government Intervention in a Developing Country: The Indonesian Story. J. of Int’l Entrepreneurship 6, 147–167 (2008)
Frame, W.S., Srinivasan, A., Woosley, L.: The Effect of Credit Scoring on Small-Business Lending. Journal of Money, Credit, and Banking 33(3), 813–825 (2001)
Dinh, T.H.T., Kleimeier, S.: A Credit Scoring Model for Vietnam’s Retail Banking Market. International Review of Financial Analysis 16(5), 471–495 (2007)
Abdou, H., Pointon, J., El-Masry, A.: Neural Nets versus Conventional Techniques in Credit Scoring in Egyptian Banking. Exp. Syst. with App. 35(3), 1275–1292 (2008)
Chye, K.H., Chin, T.W., Peng, G.C.: Credit Scoring Using Data Mining Techniques. Singapore Management Review 26(2), 25–47 (2004)
Wu, C., Wang, X.-M.: A Neural Network Approach for Analyzing Small Business Lending Decisions. Journal Review of Quantitative Finance and Accounting 15(3), 259–276 (2000)
Tsaih, R., Liu, Y.-J., Lien, Y.-L.: Credit Scoring System for Small Business Loans. Decision Support Systems 38(1), 91–99 (2004)
Lehmann, B.: Is It Worth the While? The Relevance of Qualitative Information in Credit Rating. In: SSRN eLibrary (2003)
Ikasari, N., Hadzic, F., Dillon, T.S.: Incorporating Qualitative Information for Credit Risk Assessment through Frequent Subtree Mining for XML. In: Tagarelli, A. (ed.) XML Data Mining: Models, Method, and Applications. IGI Global (2012)
Hadzic, F.: A Structure Preserving Flat Data Format Representation for Tree-Structured Data. In: Cao, L., Huang, J.Z., Bailey, J., Koh, Y.S., Luo, J. (eds.) PAKDD Workshops 2011. LNCS, vol. 7104, pp. 221–233. Springer, Heidelberg (2012)
Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., Euler, T.: YALE: Rapid Prototyping for Complex Data Mining Tasks. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, PA, USA (2006)
Zaki, M.J.: Efficiently Mining Frequent Trees in a Forest: Algorithms and Applications. IEEE Transactions on Knowledge and Data Engineering 17(8), 1021–1035 (2005)
Chi, Y., Nijssen, S., Muntz, R.R., Kok, J.N.: Frequent subtree mining - An Overview. Fundamenta Informaticae, Special Issue on Graph and Tree Mining 6(1-2), 161–198 (2005)
Hadzic, F., Tan, H., Dillon, T.S.: Mining of Data with Complex Structures. SCI, vol. 333. Springer, Heidelberg (2011)
Chi, Y., Yang, Y., Xia, Y., Muntz, R.R.: CMTreeMiner: Mining Both Closed and Maximal Frequent Subtrees. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 63–73. Springer, Heidelberg (2004)
Hadzic, F., Hecker, M., Tagarelli, A.: XML Document Clustering Using Structure-Preserving Flat Representation of XML Content and Structure. In: Tang, J., King, I., Chen, L., Wang, J. (eds.) ADMA 2011, Part II. LNCS, vol. 7121, pp. 403–416. Springer, Heidelberg (2011)
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Ikasari, N., Hadzic, F. (2012). An Assessment on Loan Performance from Combined Quantitative and Qualitative Data in XML. In: Ganascia, JG., Lenca, P., Petit, JM. (eds) Discovery Science. DS 2012. Lecture Notes in Computer Science(), vol 7569. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33492-4_22
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DOI: https://doi.org/10.1007/978-3-642-33492-4_22
Publisher Name: Springer, Berlin, Heidelberg
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