Title: A machine learning-based credit lending eligibility prediction and suitable bank recommendation: an Android app for entrepreneurs

Authors: Jakia Parvin; Mahfuzulhoq Chowdhury

Addresses: Computer Science and Engineering Department, Chittagong University of Engineering and Technology, Chittagong-4349, Bangladesh ' Computer Science and Engineering Department, Chittagong University of Engineering and Technology, Chittagong-4349, Bangladesh

Abstract: In Bangladesh, men and women are entering business not only to earn money but also to change their social conditions. Capital for conducting business is a big challenge for both male and female entrepreneurs. However, due to the lack of a proper loan eligibility system, both male and female entrepreneurs faced several problems regarding getting loans. Most entrepreneurs are unwilling to take loans from banks because their loan applications are rejected for various reasons. To overcome these challenges, in this paper, an automated recommendation system has been provided in a mobile application. This paper collects a real-time dataset for loan approval prediction systems. The system also develops a prediction model using machine learning algorithms that predict an entrepreneur's loan eligibility. The android application offers recommendations for a suitable bank for an eligible entrepreneur based on the prediction model and user data. The presented results confirm the necessity of our proposed system.

Keywords: loan; entrepreneur; prediction; classification algorithm; machine learning; mobile application.

DOI: 10.1504/IJAMS.2023.133698

International Journal of Applied Management Science, 2023 Vol.15 No.3, pp.238 - 257

Received: 13 Aug 2022
Accepted: 22 Jun 2023
Published online: 29 Sep 2023 *

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