Abstract
The ability to predict small businesses’ future loan performance based on submitted loan applications is crucial for Indonesian rural banks. The small capacity of these particular banks requires an efficient approach to extract knowledge from structured (quantitative) and unstructured (qualitative) type of credit information. The eXtensible Markup Language (XML) is used to organize this complementary credit data from an Indonesian rural bank. The credit performance evaluation application presented utilizes a mapping approach to preserve structural aspects of data within a format on which wider selections of data mining techniques are applied. Results from decision tree and association rule mining algorithms demonstrate the potential of the approach to generate reliable and valid patterns useful for evaluation of existing lending policy.
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Rhyne E, Otero M (1992) Financial services for microenterprises: principles and institutions. World Dev 20(11):1561–1571
Braverman A, Guasch JL (1986) Rural credit markets and institutions in developing countries: lessons for policy analysis from practice and modern theory. World Dev 14(10–11):1253–1267
Prior F, Argandona A (2009) Credit accessibility and corporate social responsibility in financial institutions: the case of microfinance. Bus Ethics A Eur Rev 18(4):349–363
Indonesian Bank Statistics (2011) Bank Indonesia, Jakarta, Indonesia
Berger AN, Klapper LF, Udell GF (2001) The ability of banks to lend to informationally opaque small businesses. J Bank Finance 25(12):2127–2167
Tsaih R, Liu Y-J, Liu W, Lien Y-L (2004) Credit scoring system for small business loans. Decis Support Syst 38(1):91–99
Wu C, Wang X-M (2000) A neural network approach for analyzing small business lending decisions. J Rev Quant Finance Account 15(3):259–276
Ikasari N, Hadzic F, Dillon TS (2011) 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, Philadelphia, pp 467–503
Hadzic F (2012) A structure preserving flat data format representation for tree-structured data. In: Cao L, Huang JZ, Bailey J, Koh YS, Luo J (eds) Lecture notes in computer science, vol 7104. Springer, Heidelberg, pp 221–233
Ikasari N, Hadzic F (2012) Assessment of micro loan payment using structured data mining techniques: the case of Indonesian people’ credit bank. In: Ao SI, Gelman L, Hukins DW, Hunter A, Korsunsky AM (eds) Lecture notes in engineering and computer science: proceedings of the world congress on engineering 2012, WCE 2012. London, UK, pp 511–517, 4–6 July 2012
Dinh THT, Kleimeier S (2007) A credit scoring model for Vietnam’s retail banking market. Int Rev Financial Anal 16(5):471–495
Abdou H, Pointon J, El-Masry A (2008) A Neural nets versus conventional techniques in credit scoring in Egyptian banking. Expert Syst Appl 35(3):1275–1292
Chye KH, Chin TW, Peng GC (2004) Credit scoring using data mining techniques. Singap Manag Rev 26(2):25–47
Edminster RH (1971) Financial ratios and credit scoring for small business loans. J Commer Bank Lend September:10–23
Eisenbeis RA (1978) Problems in applying discriminant analysis in credit scoring models. J Bank Finance 2(3):205–219
Altman E, Sabato G (2007) Modelling credit risk for SMEs: evidence from the U.S. market. Abacus 43(3):332–357
Bensic M, Sarlija N, Zekic-Susac M (2005) Modelling small-business credit scoring by using logistic regression, neural networks and decision trees. Intell Syst Account Finance Manag 13:133–150
Lehmann B (2003) Is it worth the while?. The relevance of qualitative information in credit rating, SSRN eLibrary
Hadzic F, Tan H, Dillon T (2011) Mining of data with complex structures. In: Studies in computational intelligence series, vol 333. Springer, Berlin
Chi Y, Nijssen S, Muntz RR, Kok JN (2005) Frequent subtree mining—an overview. Fundamenta Inform Special Issue Graph Tree Min 66(1–2):161–198
Wang K, Liu H (1998) Discovering typical structures of documents: a road map approach. In: Proceedings of the 21st annual international ACM SIGIR conference on research and development in information retrieval—SIGIR ’98, Melbourne, Australia, pp 146–154, 24–28 August 1998
Zaki MJ (2005) Efficiently mining frequent trees in a forest: algorithms and applications. IEEE Trans Knowl Data Eng 17(8):1021–1035
Han J, Kamber M (2006) Data mining: concepts and techniques, 2nd edn. Morgan Kaufmann Publishers, California
Holmes G, Donkin A, Witten IH (1994) WEKA: a machine learning workbench. In: Intelligent information systems, 1994. Proceedings of the 1994 second Australian and New Zealand conference, pp 357–361, 29 Nov–2 Dec 1994
Agrawal R, Srikant R (1994) Fast algorithms for mining association rules in large databases. In VLDB ’94 Proceedings of the 20th international conference on very large data bases, San Fransisco, pp 487–499
Quinlan R (1993) C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc, San Fransisco
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Ikasari, N., Hadzic, F. (2013). Structured Data Mining for Micro Loan Performance Prediction: The Case of Indonesian Rural Bank. In: Yang, GC., Ao, Sl., Gelman, L. (eds) IAENG Transactions on Engineering Technologies. Lecture Notes in Electrical Engineering, vol 229. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6190-2_49
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DOI: https://doi.org/10.1007/978-94-007-6190-2_49
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