Credit risk assessment using machine learning techniques

Varsha Aithal, Roshan David Jathanna

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


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 languageEnglish
Pages (from-to)3482-3486
Number of pages5
JournalInternational Journal of Innovative Technology and Exploring Engineering
Issue number1
Publication statusPublished - 11-2019

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Civil and Structural Engineering
  • Mechanics of Materials
  • Electrical and Electronic Engineering


Dive into the research topics of 'Credit risk assessment using machine learning techniques'. Together they form a unique fingerprint.

Cite this