Credit Card Deception Recognition Using Random Forest Machine Learning Algorithm

Abstract

INTRODUCTION: The credit card deception poses a global threat, resulting in significant monetary losses and identity theft. Detecting fraudulent transactions promptly is crucial for mitigating these losses. Machine learning algorithms, specifically the random forest algorithm, show promise in addressing this issue.
 OBJECTIVES: This research paper presents a comprehensive study of numerous machine learning algorithms for credit card deception recognition, focusing on the random forest algorithm.
 METHODS: To tackle the increasing fraud challenges and the need for more effective detection systems, we develop an advanced credit card deception detection system utilizing machine learning algorithms. We evaluate our system's performance using precision, recall, & F1-score metrics. Additionally, we provide various insights into the key features for fraud detection, empowering financial institutions to enhance their detection systems. The paper follows a structured approach.
 RESULTS: We review existing work on credit card fraud detection, detail the dataset and pre-processing steps, present the random forest algorithm and its application to fraud detection, compare its performance against other algorithms, discuss fraud detection challenges, and propose effective solutions.
 CONCLUSION: Finally, we conclude the research paper and suggest potential areas for future research. Our experiments demonstrate that the random forest algorithm surpasses other machine learning algorithms in accuracy, precision, recall, & F1-score. Moreover, the system effectively addresses challenges like imbalanced data and high-dimensional feature spaces. Our findings offer valuable insights into the most relevant features for fraud detection empowering financial organizations to improve their fraud detection capabilities.