The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Machine Learning Techniques-Based Banking Loan Eligibility Prediction
|
|
Author(s): Anjali Agarwal (Amity Institute of Technology, Amity University, Kolkata, India), Roshni Rupali Das (Amity Institute of Technology, Amity University, Kolkata, India)and Ajanta Das (Amity University, Kolkata, India)
Copyright: 2022
Volume: 14
Issue: 2
Pages: 19
Source title:
International Journal of Distributed Artificial Intelligence (IJDAI)
Editor(s)-in-Chief: Firas Abdulrazzaq Raheem (University of Technology - Iraq, Iraq)and Israa AbdulAmeer AbdulJabbar (University of Technology - Iraq, Iraq)
DOI: 10.4018/IJDAI.313935
Purchase
|
Abstract
In our daily life, it is difficult to meet financial demand while in crisis. This financial crisis may be solved with financial assistance from the banks. The financial assistance is nothing but availing loan from the bank with proper agreement to repay the amount including calculated interest within the loan approved tenure. The customer can only avail loans against the submission of some valid and important supportive documents. However, although the customer is aware of the whole process of repayment and installment along with loan approval tenure, most of the time it is hard to get the approved loan within a shorter period. Therefore, the objective of this paper is to automate this manual and long process by predicting the chance of approval of the loan. The novelty of this research article is to apply machine learning techniques and classification algorithms to predict loan eligibility through an automatic online loan application process
Related Content
|
Digvijay Pandey, Subodh Wairya.
© 2022.
11 pages.
|
|
Upendra Kumar, Pawan Kumar Tiwari, Tejasvi Mishra, Lalita Jaiswar, Safiya Ali.
© 2022.
16 pages.
|
|
Anjali Agarwal, Roshni Rupali Das, Ajanta Das.
© 2022.
19 pages.
|
|
Stephen Opoku Oppong, Benjamin Ghansah, Evans Baidoo, Wilson Osafo Apeanti, Daniel Danso Essel.
© 2022.
26 pages.
|
|
Mohamed Merabet, Ali Kourtiche.
© 2022.
18 pages.
|
|
Binay Kumar Pandey, Digvijay Pandey, Ashi Agarwal.
© 2022.
14 pages.
|
|
Khadidja Bouchenga, Bouabdellah Kechar, Vincent Rodin.
© 2022.
23 pages.
|
|
|