Structured Data Mining for Micro Loan Performance Prediction: The Case of Indonesian Rural Bank

Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 229)

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.

Keywords

Credit performance evaluation Data mining techniques  Database structure model Indonesian rural bank Loan performance XML 

References

  1. 1.
    Rhyne E, Otero M (1992) Financial services for microenterprises: principles and institutions. World Dev 20(11):1561–1571CrossRefGoogle Scholar
  2. 2.
    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):1253–1267CrossRefGoogle Scholar
  3. 3.
    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):349–363Google Scholar
  4. 4.
    Indonesian Bank Statistics (2011) Bank Indonesia, Jakarta, IndonesiaGoogle Scholar
  5. 5.
    Berger AN, Klapper LF, Udell GF (2001) The ability of banks to lend to informationally opaque small businesses. J Bank Finance 25(12):2127–2167CrossRefGoogle Scholar
  6. 6.
    Tsaih R, Liu Y-J, Liu W, Lien Y-L (2004) Credit scoring system for small business loans. Decis Support Syst 38(1):91–99CrossRefGoogle Scholar
  7. 7.
    Wu C, Wang X-M (2000) A neural network approach for analyzing small business lending decisions. J Rev Quant Finance Account 15(3):259–276CrossRefGoogle Scholar
  8. 8.
    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 467–503Google Scholar
  9. 9.
    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 221–233Google Scholar
  10. 10.
    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 2012Google Scholar
  11. 11.
    Dinh THT, Kleimeier S (2007) A credit scoring model for Vietnam’s retail banking market. Int Rev Financial Anal 16(5):471–495CrossRefGoogle Scholar
  12. 12.
    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):1275–1292CrossRefGoogle Scholar
  13. 13.
    Chye KH, Chin TW, Peng GC (2004) Credit scoring using data mining techniques. Singap Manag Rev 26(2):25–47Google Scholar
  14. 14.
    Edminster RH (1971) Financial ratios and credit scoring for small business loans. J Commer Bank Lend September:10–23Google Scholar
  15. 15.
    Eisenbeis RA (1978) Problems in applying discriminant analysis in credit scoring models. J Bank Finance 2(3):205–219CrossRefGoogle Scholar
  16. 16.
    Altman E, Sabato G (2007) Modelling credit risk for SMEs: evidence from the U.S. market. Abacus 43(3):332–357CrossRefGoogle Scholar
  17. 17.
    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 13:133–150CrossRefGoogle Scholar
  18. 18.
    Lehmann B (2003) Is it worth the while?. The relevance of qualitative information in credit rating, SSRN eLibraryGoogle Scholar
  19. 19.
    Hadzic F, Tan H, Dillon T (2011) Mining of data with complex structures. In: Studies in computational intelligence series, vol 333. Springer, BerlinGoogle Scholar
  20. 20.
    Chi Y, Nijssen S, Muntz RR, Kok JN (2005) Frequent subtree mining—an overview. Fundamenta Inform Special Issue Graph Tree Min 66(1–2):161–198Google Scholar
  21. 21.
    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 1998Google Scholar
  22. 22.
    Zaki MJ (2005) Efficiently mining frequent trees in a forest: algorithms and applications. IEEE Trans Knowl Data Eng 17(8):1021–1035CrossRefGoogle Scholar
  23. 23.
    Han J, Kamber M (2006) Data mining: concepts and techniques, 2nd edn. Morgan Kaufmann Publishers, CaliforniaMATHGoogle Scholar
  24. 24.
    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 1994Google Scholar
  25. 25.
    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 487–499Google Scholar
  26. 26.
    Quinlan R (1993) C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc, San FransiscoGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.School of Economics and Finance, Curtin Business SchoolCurtin UniversityPerthAustralia
  2. 2.Faculty of Social and Political ScienceUniversity of IndonesiaDepokIndonesia
  3. 3.BentleyAustralia
  4. 4.Department of Computing, Faculty of Science and EngineeringCurtin UniversityPerthAustralia
  5. 5.Building 314-New Technologies, Bentley CampusCurtin UniversityPerthAustralia

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