Credit Card Fraud Detection using Machine Learning
Hriday Shetty
Credit card fraud poses a significant threat to financial institutions, businesses, and consumers, necessitating advanced detection mechanisms to safeguard against unauthorized transactions. This research explores innovative approaches to credit card fraud detection using machine learning and data analytics. The study leverages a comprehensive dataset sourced from real-world transactions to develop and evaluate the effectiveness of fraud detection models. The research focuses on feature selection, model optimization, and the integration of emerging technologies in enhancing the accuracy and efficiency of detection systems. Results indicate promising performance, with the proposed models exhibiting notable success in identifying fraudulent activities. The findings contribute to the ongoing discourse on bolstering the resilience of financial systems against the evolving landscape of credit card fraud. The implications of the research extend to industry stakeholders, offering insights into refining existing detection strategies and inspiring future developments in the pursuit of heightened security measures.
Article Details
Unique Paper ID: 164667

Publication Volume & Issue: Volume 10, Issue 12

Page(s): 2408 - 2417
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