Evaluating Performance Of Credit Card Fraud Detection Using CatBoost And Machine Learning Methods

Arjun Parashar , Ananya Bhardwaz , Rishabh Sharma

Credit card fraud is a major problem in the financial services industry. Thousands of dollars are lost each year due to credit card fraud. Due to privacy concerns, there isn't enough research that actually validates credit card information. This article uses machine learning algorithms to detect credit card fraud. First use the template. We will use another way of using CatBoost. By applying this algorithm to existing models, we aim to further improve the performance of these models. Finally, we compare the performance of the base model and the powered model with CatBoost and analyze the results. We expect the augmented model to outperform the base model while reducing false positives, especially in fraud detection. Overall, the project aims to demonstrate the importance of feature engineering and algorithm selection in credit card fraud and the effectiveness of the CatBoost algorithm in improving model performance. Experimental results show that the CatBoost method has good accuracy in detecting credit card

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"Evaluating Performance Of Credit Card Fraud Detection Using CatBoost And Machine Learning Methods", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.9, Issue 1, page no.584 - 589, January-2024, Available :https://ijsdr.org/papers/IJSDR2401084.pdf

Volume 9 Issue 1, January-2024

Pages : 584 - 589

Paper Reg. ID: IJSDR_206439

Published Paper Id: IJSDR2401084

Downloads: 000347334

Research Area: Computer Science & Technology 

Country: Ghaziabad, Uttar Pradesh, India

Published Paper PDF: https://ijsdr.org/papers/IJSDR2401084

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR2401084

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJSDR(IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition