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IJSTR >> Volume 9 - Issue 6, June 2020 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Sample Model for the Prediction of Default Risk of Loan Applications Using Data Mining

[Full Text]



Princess May G. Subia, Angelo C. Galapon



Data Mining, Credit History, Classification, Attribute, Dataset, Model, Credit Risk, Loan, Weka, Borrower, Banking Sector. Algorithm.



Data mining is becoming a strategically important area in the banking sector. Where volumes of electronic data are stored, and where the number of transactions is increasing rapidly. Using Data Mining, it is possible to collect some interesting patterns and knowledge base, transforming into useful information that can be used to minimize the risk in bank loans. In this paper, Data mining was used as a tool to extract relevant information from existing credit data of a bank to build a model that can be used to evaluate and decide whether a borrower is a right candidate for a loan, or if there is a high risk of default which will be run using an open-source machine learning software called Waikato Environment for Knowledge Analysis (WEKA). Also discussed is the method in constructing the model and the recognition of its accuracy rate using the classification algorithm J48.



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