Abstract
Massive usage of credit cards has caused an escalation of fraud. Usage of credit cards has resulted in the growth of online business advancement and ease of the e-payment system. The use of machine learning (methods) are adapted on a larger scale to detect and prevent fraud. ML algorithms play an essential role in analysing customer data. In this research article, we have conducted a comparative analysis of the literature review considering the ML techniques for credit card fraud detection (CCFD) and data confidentiality. In the end, we have proposed a hybrid solution, using the neural network (ANN) in a federated learning framework. It has been observed as an effective solution for achieving higher accuracy in CCFD while ensuring privacy.
| Original language | English |
|---|---|
| Pages (from-to) | 55-68 |
| Number of pages | 14 |
| Journal | Human-Centric Intelligent Systems |
| Volume | 2 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 5 May 2022 |
Keywords
- Artificial neural network (ANN)
- Blockchain
- Credit card fraud
- Federated learning
- Privacy-preserving
- Random forest (RF) method
- Support vector machine (SVM)
ASJC Scopus subject areas
- Information Systems
- Artificial Intelligence
- Computer Science (miscellaneous)
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