Credit Risk Assessment using Machine Learning Techniques
Varsha Aithal1, Roshan David Jathanna2
1Varsha Aithal*, Dept. of Computer science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.
2Roshan David Jathanna, Dept. of Computer science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.
Manuscript received on October 14, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP:3482-3486 | Volume-9 Issue-1, November 2019. | Retrieval Number: A4936119119/2019©BEIESP | DOI: 10.35940/ijitee.A4936.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Classification Algorithm, Credit Risk Evaluation, Machine learning, supervised learning.
Scope of the Article: Classification