Loan Default Prediction Model Using Sample, Explore, Modify, Model, and Assess (SEMMA)

摘要

The purpose of this study is to provide a comprehensive research and to develop a model to predict the loan defaults. This kind of models becomes inevitable as the issue of bad loans are very much critical in the financial sector especially in micro financing banks of various underdevelopedand developed countries. To cope up with this problem a comprehensive literature review was done to study the significant factors that leads to this issue. Moreover, these reviewed studies were critically focused towards applying data mining techniques for the prediction and classificationof the loan defaults. This study used methodologies named KDD, CRISP-DM and SEMMA. While in the experimentation phase, three different data mining techniques were applied for the proposed model and their performances were evaluated on various parameters. Based on these parameters, the bestmethod was selected, explained and suggested because of its significant characteristics regarding the prediction of the loan defaults in the financial sector.