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

  • Novita Ikasari
  • Fedja Hadzic
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 229)


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.


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


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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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