i-manager Publications

Credit Card Fraud Detection System

Ernest Makombe*, Fanny Chatola**, Pradeep J.***, G. Glorindal****

*-**** DMI-St. John the Baptist University, Malawi.

Periodicity:July - December'2023
DOI : https://doi.org/10.26634/jdf.1.2.20064

Abstract

Credit card fraud is a significant problem for financial institutions and cardholders and can result in significant financial losses and damage to reputation. Detecting and preventing fraud in credit card transactions is critical to minimizing losses and maintaining customer trust. The main objective of this project is to build a model (a web-based system) that can effectively identify fraudulent transactions. This involves collecting and processing large datasets of credit card transactions, including both legitimate and fraudulent transactions, and using machine learning algorithms like decision trees and random forests to train a model to recognize patterns and anomalies. In addition, the system will have strong user authentication protocols that must be in place to prevent unauthorized access to the system.

Keywords

Fraud Transaction, Financial Institutions, Machine Learning, User Authentication, Decision Trees, Random Forest, Transaction Datasets, Training.

How to Cite this Article?

Makombe, E., Chatola, F., Pradeep, J., and Glorindal, G. (2023). Credit Card Fraud Detection System. i-manager’s Journal on Digital Forensics & Cyber Security, 1(2), 12-19. https://doi.org/10.26634/jdf.1.2.20064

References

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Credit Card Fraud Detection System

Abstract

Keywords

How to Cite this Article?

References

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