Advancing Credit Card Fraud Detection: A Review of Machine Learning Algorithms and the Power of Light Gradient Boosting
Farhan Aslam · American Journal of Computer Science and Technology · 2024
The surge in credit card transactions has necessitated the implementation of robust security measures to combat the ever-evolving threat of fraud. Traditional methods of fraud detection have proven inadequate in identifying intricate fraud patterns, prompting the adoption of machine learning (ML) as a vital tool in the fight against fraud. This article delves into recent research findings and proposes innovative strategies to elevate the current state of the art in fraud detection using ML techniques. The study critically assesses the efficacy of diverse ML algorithms in detecting credit card fraud, comparing their accuracy and performance while exploring the incorporation of recent research insights to further enhance their capabilities. The article begins by highlighting the growing significance of ML in addressing the challenges posed by fraudulent credit card transactions. It underscores the limitations of conventional fraud detection methods, emphasizing the need for adaptive and data-driven solutions to stay ahead of increasingly sophisticated fraudsters. A comprehensive analysis of various ML algorithms used in credit card fraud detection forms the core of this study. By examining the strengths and weaknesses of algorithms such as Random Forest, Support Vector Machine, and Neural Networks, the article aims to provide a holistic view of their performance and suitability in real-world scenarios. It identifies the key parameters that impact algorithmic performance and suggests optimal configurations for improved accuracy. One of the focal points of this research is the exploration of the Light Gradient Boosting Machine (LGBM) as a promising algorithm for credit card fraud detection. The article elucidates the distinct advantages of LGBM over other ML algorithms, including its efficiency in handling large datasets, ability to capture complex fraud patterns, and fast training times. Practical insights are offered on how LGBM can be implemented and fine-tuned to maximize its potential in fraud detection. In conclusion, this article contributes significantly to the ongoing pursuit of enhanced fraud detection mechanisms and the prevention of financial loss for consumers. By critically evaluating the effectiveness of ML algorithms and highlighting the potential of LGBM, it offers valuable insights to researchers, practitioners, and financial institutions seeking to fortify their defenses against credit card fraud. As fraudsters continue to adapt and evolve, the application of advanced ML techniques becomes increasingly imperative in safeguarding the integrity of financial transactions and preserving trust in the digital payment ecosystem.