International Journal of Advanced Research in Computer and Communication Engineering

A monthly peer-reviewed online and print journal

ISSN Online 2278-1021
ISSN Print 2319-5940

Since 2012

Abstract: The banking industry is changing and new trends of using technology in banking are emerging. Credit risk assessment has an important role in the banking sector. Various researches have been done to automate the process of predicting the loan default probability to speed up the process and reduce human errors. In this paper, a comparative study of various methods to identify the rightful customers for the bank. The paper also demonstrates how social media networks can be useful for gathering data which can be used for finding the loan eligibility of the user. The outcome of this paper is to find which method or algorithm gives the most accurate result while prediction loan eligibility of a user.

Keywords: Machine Learning, Credit Risk, Personalization, Social Networks


PDF | DOI: 10.17148/IJARCCE.2021.10337

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