Fraudulent credit card transaction detection using soft computing techniques
Nowadays, fraudulent or deceitful activities associated with financial transactions, predominantly using credit cards have been increasing at an alarming rate and are one of the most prevalent activities in finance industries, corporate companies, and other government organizations. It is therefore...
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| Main Authors: | , , , , |
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2021-09-01.
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| Online Access: | Get fulltext |
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| LEADER | 02688 am a22003373u 4500 | ||
|---|---|---|---|
| 001 | ijeecs25850_15409 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Priyadarshini, Aishwarya |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Mishra, Sanhita |e author |
| 700 | 1 | 0 | |a Mishra, Debani Prasad |e author |
| 700 | 1 | 0 | |a Salkuti, Surender Reddy |e author |
| 700 | 1 | 0 | |a Mohanty, Ramakanta |e author |
| 245 | 0 | 0 | |a Fraudulent credit card transaction detection using soft computing techniques |
| 260 | |b Institute of Advanced Engineering and Science, |c 2021-09-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/25850 | ||
| 520 | |a Nowadays, fraudulent or deceitful activities associated with financial transactions, predominantly using credit cards have been increasing at an alarming rate and are one of the most prevalent activities in finance industries, corporate companies, and other government organizations. It is therefore essential to incorporate a fraud detection system that mainly consists of intelligent fraud detection techniques to keep in view the consumer and clients' welfare alike. Numerous fraud detection procedures, techniques, and systems in literature have been implemented by employing a myriad of intelligent techniques including algorithms and frameworks to detect fraudulent and deceitful transactions. This paper initially analyses the data through exploratory data analysis and then proposes various classification models that are implemented using intelligent soft computing techniques to predictively classify fraudulent credit card transactions. Classification algorithms such as K-Nearest neighbor (K-NN), decision tree, random forest (RF), and logistic regression (LR) have been implemented to critically evaluate their performances. The proposed model is computationally efficient, light-weight and can be used for credit card fraudulent transaction detection with better accuracy. | ||
| 540 | |a Copyright (c) 2021 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |a Data distribution; Exploratory data analysis; Fraud detection; Outliers; | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 23, No 3: September 2021; 1634-1642 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v23.i3 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/25850/15409 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/25850/15409 |z Get fulltext |