Journal of Emerging Trends in Engineering and Applied Sciences: Research Areas

Article Title: Leveraging Artificial Intelligence And Machine Learning For Credit Card Fraud Prevention And Investigation: A Study On The Application Of Random Forest Classifier In Fraud Detection
by Robinson Onajite Otubu

Abstract:
Credit card fraud remains a major challenge for financial institutions, with substantial financial losses and reputational damage to businesses. Traditional fraud detection systems, primarily rule-based, are often inefficient in identifying novel fraud patterns and managing the massive volume of transactions in real time. Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) offer promising solutions for improving the accuracy and efficiency of fraud detection. This research explores the application of the Random Forest Classifier, a powerful ensemble learning algorithm, for detecting credit card fraud. By leveraging historical transaction data, the study evaluates the model's performance in identifying fraudulent transactions while minimizing false positives. The results indicate that the Random Forest Classifier achieved an accuracy of 97.73%, with a precision of 83.33% and a recall of 88.24%, demonstrating its effectiveness in fraud detection. The study highlights the potential of AI and ML to revolutionize fraud prevention systems, offering insights into their practical implementation for real-world applications. Furthermore, it discusses the challenges of handling imbalanced datasets and suggests future improvements to enhance the model�s performance. This research contributes to the growing body of knowledge on machine learning-driven fraud detection systems and underscores the importance of adaptive, data-driven approaches in combating credit card fraud. __________________________________________________________________________________________ Keywords:

Keywords: Artificial Intelligence, Machine Learning, Credit Card, Random Forest Classifier, Fraud

Download full paper