Credit card fraud detection using CNN and LSTM
| Dublin Core | PKP Metadata Items | Metadata for this Document | |
| 1. | Title | Title of document | Credit card fraud detection using CNN and LSTM |
| 2. | Creator | Author's name, affiliation, country | Nishant Upadhyay; Sharda University; India |
| 2. | Creator | Author's name, affiliation, country | Nidhi Bansal; Manav Rachna International Institute of Research and Studies (Deemed to be University); India |
| 2. | Creator | Author's name, affiliation, country | Divya Rastogi; Sharda University; India |
| 2. | Creator | Author's name, affiliation, country | Rekha Chaturvedi; Manipal University Jaipur; India |
| 2. | Creator | Author's name, affiliation, country | Mohammad Asim; Sharda University; India |
| 2. | Creator | Author's name, affiliation, country | Suraj Malik; IIMT University; India |
| 2. | Creator | Author's name, affiliation, country | Khel Prakash Jayant; Raj Kumar Goel Institute of Technology; India |
| 2. | Creator | Author's name, affiliation, country | Abhay Kumar Vajpayee; Institute of Engineering and Technology Sitapur; India |
| 3. | Subject | Discipline(s) | computer science |
| 3. | Subject | Keyword(s) | CNN; Credit card; Fraud detection; LSTM; Online transaction |
| 4. | Description | Abstract | Credit card fraud is an evolving problem with the fraudsters developing new technologies to perform fraud. Fraudsters have found diverse ways to make a fraud transaction to the card holder. Thus, detecting suspicious behavior of a card is critical for preventing fraudulent transactions to happen. Artificial intelligence techniques, in particular deep learning algorithms can tackle these credit card fraud attacks by identifying patterns that predict transactions as fraud or legitimate. One-dimensional convolutional neural network (1D CNN) and long short-term memory (LSTM) both performs well on the sequential data especially on transactions data, yet there are not many studies done on combining these two algorithms to make an effective fraud detection approach. However, the dataset is highly imbalanced containing only 492 fraud transaction out of two lacs transactions. In this experimental study, firstly datasets will get prepared by using different sampling techniques along with their hybrid techniques secondly, observing the performance of individual CNN and LSTM on the datasets, finally on those datasets in which CNN and LSTM are performing well, by implementing ensemble on those data. The performance of the ensembles is observed using the performance metrics namely accuracy, F1-score, precision and recall. In the proposed experimental study, getting the F1-score of 99.96% and 99.89% in ensemble: early fusion and ensemble: late fusion respectively. |
| 5. | Publisher | Organizing agency, location | Institute of Advanced Engineering and Science |
| 6. | Contributor | Sponsor(s) | |
| 7. | Date | (YYYY-MM-DD) | 2025-05-01 |
| 8. | Type | Status & genre | Peer-reviewed Article |
| 8. | Type | Type | |
| 9. | Format | File format | |
| 10. | Identifier | Uniform Resource Identifier | https://ijeecs.iaescore.com/index.php/IJEECS/article/view/38373 |
| 10. | Identifier | Digital Object Identifier (DOI) | http://doi.org/10.11591/ijeecs.v38.i2.pp1402-1410 |
| 11. | Source | Title; vol., no. (year) | Indonesian Journal of Electrical Engineering and Computer Science; Vol 38, No 2: May 2025 |
| 12. | Language | English=en | en |
| 14. | Coverage | Geo-spatial location, chronological period, research sample (gender, age, etc.) | |
| 15. | Rights | Copyright and permissions |
Copyright (c) 2025 Nidhi Bansal This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. |