Structured data mining for micro loan performance prediction: The case of Indonesian rural bank

Novita Ikasari, Fedja Hadzic

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationIAENG Transactions on Engineering Technologies - Special Volume of the World Congress on Engineering 2012
Pages641-653
Number of pages13
DOIs
Publication statusPublished - 1 Aug 2013
Event2012 World Congress on Engineering, WCE 2012 - London, United Kingdom
Duration: 4 Jul 20126 Jul 2012

Publication series

NameLecture Notes in Electrical Engineering
Volume229 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference2012 World Congress on Engineering, WCE 2012
CountryUnited Kingdom
CityLondon
Period4/07/126/07/12

Keywords

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

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