Loan Default Forecasting using Data Mining

Abstract

Estimation or assessment of default on a debt is a crucial process that should be carried out by banks to help them to assess if a loan applicant can be a defaulter at a later phase so that they process the application and decide whether to approve the loan or not. The conclusion derived from such assessments helps banks and other financial institutions to lessen their losses and eventually increase the number of credits. Hence, it becomes vital to construct a model that will take into account the different aspects of an applicant and derive a result regarding the concerned applicant. All available means to loan the money from their illicit activities are used for criminal activities in today's technology-based realm. The increasing number of bad debts resulting from commercial banks' loans reflects the growing problem of distraught banks within the economic system. We have used data mining algorithms to predict the likely defaulters from a dataset that contains information about home loan applications, thereby helping the banks for making better decisions in the future.

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