Volume : 9, Issue : 6, JUN 2023

NATIONAL CONFERENCE ON INNOVATIONS IN COMPUTING TECHNOLOGIES (NCICT'23)

CREDIT CARD FRAUD DETECTION

AKALYA SK, ANUVARSHINI SS, AKSHAYA BS, ABHINAV S, SATHISH KUMAR S

Abstract

Fraud detection with Machine Learning becomes possible since it is easy to learn from historical fraud patterns and recognize them in further transactions. ML algorithms are able to find sophisticated fraud traits which humans can’t detect. Fraud models can be tackled with both supervised and unsupervised ML algorithms. In the first case, traditional classifications are used. Whereas in second case, anomaly detection techniques are used.

Article : Download PDF

Cite This Article

-

Article No : 22

Number of Downloads : 682

References

1. https://www.sciencedirect.com/science/article/p ii/S187705092030 065X

2. https://www.researchgate.net/publication/33680 0562_Credit_Card_Fraud_Detection_using_Machine_Learning_and_Data_Science

3. https://www.kaggle.com/code/marcelotc/credit card-fraud-logistic- regression-example

4. https://www.ijert.org/credit-card-fraud-detection

5. https://towardsdatascience.com/credit-card-fraud -detection-using-machine-learning-python 5b098d4a8edc

6. https://www.sciencedirect.com/science/article/p ii/S187705091500 7103

7. https://journalofbigdata.springeropen.com/articl es/10.1186/s40537-022-00573-8

8. https://www.ripublication.com/ijaer18/ijaerv13 n 24_18.pdf

9. https://asianssr.org/index.php/ajct/article/view/ 11 51

10. https://digitalcommons.aaru.edu.jo/cgi/view con tent.cgi?article=11 05&context=fcij

11. https://www.sciencepubco.com/index.php/ijet/a rticle/view/9356

12. https://fraud.net/n/five-methods-of-banking- fraud -prevention/

13. https://seon.io/resources/banking-fraud-detecti on-and-prevention/

14. https://www.crisil.com/en/home/our-business es/global-research-and-risk-solutions/our-offerings /non-financial-risk/financial- crime-management/fr aud-management/fraud-detection-and-analytics.html