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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: October 2025
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Impact Factor : 8.14
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| Paper Title: | Predicting Credit Card Defaults with Machine Learning Algorithms |
| Authors Name: | Hansraj Lawte , Laxman Pawar , Prathmesh Bondar , Gopal Malache , Shivshankar Chandsure |
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IJRTI_204694
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| Published Paper Id: | IJRTI2506083 |
| Published In: | Volume 10 Issue 6, June-2025 |
| DOI: | |
| Abstract: | With credit systems playing an increasingly important role in the worldwide economy, the correct anticipation of credit card defaults is now a must for not only financial institutions that want to limit their risks but also those who aim at creating sustainable financial solutions. The main objective of the research paper is to find a machine learning technique that can help banks recognize those clients that are likely to be a burden by not paying their card debits. The research dwells on one dataset that contains different customer attributes, such as demographic details, payment history, and credit usage behavior, thus allows the researchers to spot any patterns that may lead to a default. The authors choose to test the performance of Logistic Regression, Naive Bayes Classifier, and Random Forest, and evaluate them using accuracy, precision, recall, and F1-score that are the main metrics of the comparative model. The evaluation features a discussion of each of the models’ predictive power and also the reasons why they are good or bad, and the fact that they can be put into practice in real-world credit risk assessment. The findings certify that the application of machine learning techniques is highly beneficial considering that they can offer reliable risk forecasts. This paper not only presents the merits of using data- based techniques in credit scoring but also leads the way for more studies that would focus on the incorporation of more updated and advanced algorithms and real-time data in financial forecasting. |
| Keywords: | Logistic Regression, Naive Bayes, Random Forest, Credit Card, Visualization, Prediction, Accuracy |
| Cite Article: | "Predicting Credit Card Defaults with Machine Learning Algorithms", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 6, page no.a698-a705, June-2025, Available :http://www.ijrti.org/papers/IJRTI2506083.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2506083
Registration ID:204694
Published In: Volume 10 Issue 6, June-2025
DOI (Digital Object Identifier):
Page No: a698-a705 Country: Latur, Maharashtra, India Research Area: Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2506083 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2506083 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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