Author(s): Veeraballi Nagajyothi

Abstract: In this modern world, financial institutions are playing a very crucial role. Nowadays, banks are developing their financial reserves by providing different kinds of loans to people who are in need. At the same time, there is also a massive increase in the count of individuals requesting loans. However, banks cannot provide loans for everyone as there are only limited reserves associated with each of them. So, banks must follow some stringent verification process to approve the loan, because if the one who got his/her loan approved failed to pay back his loan it may have a direct impact on the financial reserves of the bank and also onto the banking sector. So, banks started to provide loans only for a limited set of people who are capable of repaying their loans. But finding out who is eligible for the loan is a much typical and risky process. In this project, we will develop a model to predict who is eligible for a loan in order to reduce the risk associated with the decision process and to modify the typical loan approval process into a much easier one. Moreover, we will make use of previous data of loan decisions made by the company and with the help of various data mining techniques, we will develop a loan approval decision predicting model which can draw decisions for each individual based on the information provided by them. We will use a machine-learning-based KNN, Decision-tree, Naïve Bayes algorithms to train the model. This project primary goal is to develop a loan prediction model with a better accuracy rate.

DOI: 10.33545/26633582.2020.v2.i1a.30

Pages: 32-37 | Views: 3710 | Downloads: 2910

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