@inproceedings{b601d0249da846148945c22e9c55a99c,
title = "Structured data mining for micro loan performance prediction: The case of Indonesian rural bank",
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",
author = "Novita Ikasari and Fedja Hadzic",
year = "2013",
doi = "10.1007/978-94-007-6190-2_49",
language = "English",
isbn = "9789400761896",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Verlag",
pages = "641--653",
booktitle = "IAENG Transactions on Engineering Technologies - Special Volume of the World Congress on Engineering 2012",
address = "Germany",
note = "2012 World Congress on Engineering, WCE 2012 ; Conference date: 04-07-2012 Through 06-07-2012",
}