Credit Card Fraud Detection Using AI/ML/CNN
  • Author(s): Parthib Ray ; Dr. R. Renuka Devi
  • Paper ID: 1704172
  • Page: 242-249
  • Published Date: 23-03-2023
  • Published In: Iconic Research And Engineering Journals
  • Publisher: IRE Journals
  • e-ISSN: 2456-8880
  • Volume/Issue: Volume 6 Issue 9 March-2023

In this new era of digital payments gaining momentum and a cashless world due to the current ongoing pandemic most of the payments have gone online rather than physical payments being the first choice in pre pandemic years. But as it is said every coin has two sides, credit card payments are highly risky and frauds can easily be committed by hackers and fraudsters to siphon off money from peoples account for their own personal gains. So to combat this a fraud detection machine is put in place for banks to detect such frauds and counter it accordingly. This fraud detection model is created using upcoming technologies like CNN (convolutional neural networks), Machine Learning which come under the canopy of Artificial Intelligence (AI). This model if used in a large scale on a commercial basis can reduce fraud rates to a very minimal level with a precision of about 99%. The added feature in this model is that using various contemporary machine learning algorithms and with the help of some data rectifiers the user will be able to graphically analyze the fraud rate using feature importance graphs to name a few. This software is an upgraded version of the conventional fraud detection machines currently in use in financial institutions.


Fraud, Machine Learning, Machine Learning Models, Sampling techniques, Preprocessing, AI, Precision, Accuracy, Test Data, Training Data, Threshold of Tolerance, Weighted Average, Convolutional Neural Networks, Feature Importance.


IRE Journals:
Parthib Ray , Dr. R. Renuka Devi "Credit Card Fraud Detection Using AI/ML/CNN" Iconic Research And Engineering Journals Volume 6 Issue 9 2023 Page 242-249

Parthib Ray , Dr. R. Renuka Devi "Credit Card Fraud Detection Using AI/ML/CNN" Iconic Research And Engineering Journals, 6(9)