Proceedings of the International Conference on Artificial Intelligence Techniques for Electrical Engineering Systems (AITEES 2022)

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Analysis of Fraud Detection Prediction Using Synthetic Minority Over-Sampling Technique

Authors
Uma Maheswari Ramisetty1, Venkata Nagesh Kumar Gundavarapu2, *, Akanksha Mishra3, Sravana Kumar Bali4
1Vignan’s Institute of Information Technology, Visakhapatnam, India
2JNTUA College of Engineering Pulivendula, Pulivendula, India
3Vignan’s Institute of Engineering for Women, Visakhapatnam, India
4GITAM University, Visakhapatnam, India
*Corresponding author. Email: drgvnk14@gmail.com
Corresponding Author
Venkata Nagesh Kumar Gundavarapu
Available Online 5 December 2022.
DOI
10.2991/978-94-6463-074-9_2How to use a DOI?
Keywords
Synthetic Minority Over-sampling Technique (SMOTE); eXtreme Gradient boosting; Accuracy; Precision
Abstract

Credit cards are increasingly being used in real life for a wide variety of purposes. Because of the growing number of users, the number of scammers is also growing at an accelerating rate. E-commerce fraud detection methods are critical for reducing losses. Models developed in the past using unbalanced datasets show a high degree of accuracy. The precision, recall, and weighted average precision and recall are all quite low for the models. As a result of this research, techniques such as logistic regression (LR) and random forest (RF), along with SMOTE, were developed to increase the model’s performance with imbalanced datasets. SMOTE techniques are used to balance the datasets because they are so unbalanced. SMOTE analysis has revealed that the RF with SMOTE is the best model for detecting credit card fraud, with accuracy, precision, and recall scores of 99.95%, 85.40%, 86.02%, and 85.71%, respectively.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Artificial Intelligence Techniques for Electrical Engineering Systems (AITEES 2022)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
5 December 2022
ISBN
10.2991/978-94-6463-074-9_2
ISSN
2589-4919
DOI
10.2991/978-94-6463-074-9_2How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Uma Maheswari Ramisetty
AU  - Venkata Nagesh Kumar Gundavarapu
AU  - Akanksha Mishra
AU  - Sravana Kumar Bali
PY  - 2022
DA  - 2022/12/05
TI  - Analysis of Fraud Detection Prediction Using Synthetic Minority Over-Sampling Technique
BT  - Proceedings of the International Conference on Artificial Intelligence Techniques for Electrical Engineering Systems (AITEES 2022)
PB  - Atlantis Press
SP  - 3
EP  - 12
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-074-9_2
DO  - 10.2991/978-94-6463-074-9_2
ID  - Ramisetty2022
ER  -