Fraud Transaction Detection System
  • Author(s): Sushant Agrawal
  • Paper ID: 1704592
  • Page: 84-88
  • Published Date: 05-06-2023
  • Published In: Iconic Research And Engineering Journals
  • Publisher: IRE Journals
  • e-ISSN: 2456-8880
  • Volume/Issue: Volume 6 Issue 12 June-2023
Abstract

To prevent customers from being charged for unauthorized purchases, it is crucial for credit card issuers to be able to identify fraudulent transactions. Data science, in conjunction with machine learning, plays a significant role in addressing this issue. This study focuses on utilizing machine learning to model a dataset for credit card fraud detection. The approach involves analyzing past credit card transactions, particularly those that were later identified as fraudulent, in order to assess the legitimacy of new transactions. The objective is to minimize false categorizations of fraud while accurately identifying all instances of fraudulent activity. One prominent example of categorization is the detection of credit card fraud. This approach involves analyzing and pre-processing datasets, as well as employing various anomaly detection techniques on PCA-transformed credit card transaction data.

Citations

IRE Journals:
Sushant Agrawal "Fraud Transaction Detection System" Iconic Research And Engineering Journals Volume 6 Issue 12 2023 Page 84-88

IEEE:
Sushant Agrawal "Fraud Transaction Detection System" Iconic Research And Engineering Journals, 6(12)