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.
| Original language | English |
|---|---|
| Pages (from-to) | 3482-3486 |
| Number of pages | 5 |
| Journal | International Journal of Innovative Technology and Exploring Engineering |
| Volume | 9 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 11-2019 |
| Externally published | Yes |
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All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Civil and Structural Engineering
- Mechanics of Materials
- Electrical and Electronic Engineering
Cite this
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Credit risk assessment using machine learning techniques. / Aithal, Varsha; Jathanna, Roshan David.
In: International Journal of Innovative Technology and Exploring Engineering, Vol. 9, No. 1, 11.2019, p. 3482-3486.Research output: Contribution to journal › Article
TY - JOUR
T1 - Credit risk assessment using machine learning techniques
AU - Aithal, Varsha
AU - Jathanna, Roshan David
PY - 2019/11
Y1 - 2019/11
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=85075153089&partnerID=8YFLogxK
U2 - 10.35940/ijitee.A4936.119119
DO - 10.35940/ijitee.A4936.119119
M3 - Article
AN - SCOPUS:85075153089
VL - 9
SP - 3482
EP - 3486
JO - International Journal of Innovative Technology and Exploring Engineering
JF - International Journal of Innovative Technology and Exploring Engineering
SN - 2278-3075
IS - 1
ER -