Credit Risk Assessment using Machine Learning Techniques

Aithal, Varsha and Jathanna, Roshan David (2019) Credit Risk Assessment using Machine Learning Techniques. International Journal of Innovative Technology and Exploring Engineering, 9 (1). pp. 2278-3075. ISSN 2278-3075

[img] PDF
7950.pdf - Published Version
Restricted to Registered users only

Download (516kB) | Request a copy

Abstract

Analysis of credit scoring is an effective credit risk assessment technique, which is one of the major research fields in the banking sector. Machine learning has a variety of applications in the banking sector and it has been widely used for data analysis. Modern techniques such as machine learning have provided a self-regulating process to analyze the data using classification techniques. The classification method is a supervised learning process in which the computer learns from the input data provided and makes use of this information to classify the new dataset. This research paper presents a comparison of various machine learning techniques used to evaluate the credit risk. A credit transaction that needs to be accepted or rejected is trained and implemented on the dataset using different machine learning algorithms. The techniques are implemented on the German credit dataset taken from UCI repository which has 1000 instances and 21 attributes, depending on which the transactions are either accepted or rejected. This paper compares algorithms such as Support Vector Network, Neural Network, Logistic Regression, Naive Bayes, Random Forest, and Classification and Regression Trees (CART) algorithm and the results obtained show that Random Forest algorithm was able to predict credit risk with higher accuracy.

Item Type: Article
Uncontrolled Keywords: Classification Algorithm, Credit Risk evaluation, Machine learning, supervised learning
Subjects: Engineering > MIT Manipal > Computer Science and Engineering
Depositing User: MIT Library
Date Deposited: 06 May 2020 04:13
Last Modified: 06 May 2020 04:13
URI: http://eprints.manipal.edu/id/eprint/155077

Actions (login required)

View Item View Item