Advancing Credit Card Fraud Detection: A Review of Machine Learning Algorithms and the Power of Light Gradient Boosting

Farhan Aslam

doi:doi:10.11648/ajcst.20240701.12

Research Article |

| Peer-Reviewed

Advancing Credit Card Fraud Detection: A Review of Machine Learning Algorithms and the Power of Light Gradient Boosting

Farhan Aslam

Published in American Journal of Computer Science and Technology (Volume 7, Issue 1)

Received: 4 October 2023 Accepted: 27 October 2023 Published: 1 February 2024

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

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.

Published in	American Journal of Computer Science and Technology (Volume 7, Issue 1)
DOI	10.11648/ajcst.20240701.12
Page(s)	9-12
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Fraud Detection, Machine Learning Algorithms, Credit Card Fraud, Light Gradient Boosting Machine (LGBM), Recent Research Findings, Data Preprocessing, Anomaly Detection

References

[1]	Sanober, S., Alam, I., Pande, S., Arslan, F. An enhanced secure deep learning algorithm for fraud detection in wireless communication. (n.d.) Retrieved October 3, 2023, from www.hindawi.com/journals/wcmc/2021/6079582/
[2]	Alarfaj, F., Malik, I., Khan, H., Almusallam, N. Credit card fraud detection using state-of-the-art machine learning and deep learning algorithms. (n.d.) Retrieved October 3, 2023, from ieeexplore.ieee.org/abstract/document/9755930/
[3]	Dornadula, V., Geetha, S. Credit card fraud detection using machine learning algorithms. (n.d.) Retrieved October 3, 2023, from www.sciencedirect.com/science/article/pii/S187705092030065X
[4]	Khatri, S., Arora, A., Agrawal, A. Supervised machine learning algorithms for credit card fraud detection: a comparison. (n.d.) Retrieved October 3, 2023, from ieeexplore.ieee.org/abstract/document/9057851/
[5]	Mittal, S., Tyagi, S. Performance evaluation of machine learning algorithms for credit card fraud detection. (n.d.) Retrieved October 3, 2023, from ieeexplore.ieee.org/abstract/document/8776925/
[6]	Perols, J. Financial statement fraud detection: An analysis of statistical and machine learning algorithms. (n.d.) Retrieved October 3, 2023, from publications.aaahq.org/ajpt/article-abstract/30/2/19/5673
[7]	Aslam, F. (2023). Advancing Intelligence: Unveiling the Power of Advanced Machine Learning Algorithms for Real-World Applications. Journal of Engineering Research and Reports, 25 (7), 159–165. https://doi.org/10.9734/jerr/2023/v25i7949
[8]	Thennakoon, A., Bhagyani, C. Real-time credit card fraud detection using machine learning. (n.d.) Retrieved October 3, 2023, from ieeexplore.ieee.org/abstract/document/8776942/
[9]	Aslam, F.. (2023). The Impact of Artificial Intelligence on Chatbot Technology: A Study on the Current Advancements and Leading Innovations. European Journal of Technology, 7 (3), 62 - 72. https://doi.org/10.47672/ejt.1561
[10]	Varmedja, D., Karanovic, M., Sladojevic, S. Credit card fraud detection-machine learning methods. (n.d.) Retrieved October 3, 2023, from ieeexplore.ieee.org/abstract/document/8717766/
[11]	Sailusha, R., Gnaneswar, V., Ramesh, R. Credit card fraud detection using machine learning. (n.d.) Retrieved October 3, 2023, from ieeexplore.ieee.org/abstract/document/9121114/
[12]	Anand, S., Hu, J., Liden, R., Vidyarthi, P. Leader-member exchange: Recent research findings and prospects for the future. (n.d.) Retrieved October 3, 2023, from www.torrossa.com
[13]	Langford, J., Clance, P. The imposter phenomenon: Recent research findings regarding dynamics, personality and family patterns and their implications for treatment. (n.d.) Retrieved October 3, 2023, from psycnet.apa.org/journals/pst/30/3/495/
[14]	Farhan Aslam. (2023). Role of Cloud Computing for Big Data. https://doi.org/10.5281/zenodo.8311108
[15]	Jehkonen, M., Laihosalo, M. Impact of neglect on functional outcome after stroke–a review of methodological issues and recent research findings. (n.d.) Retrieved October 3, 2023, from content.iospress.com
[16]	Aslam, F. (2023). Unleashing the Power of Cloud Computing for Big Data Management: Advantages, Challenges, and Future Prospects. Asian Journal of Research in Computer Science, 16 (3), 290–295. https://doi.org/10.9734/ajrcos/2023/v16i3363
[17]	Baumgartel, H., Reynolds, J. … personality and organisational climate variables moderate the effectiveness of management development programmes: A review and some recent research findings. (n.d.) Retrieved October 3, 2023, from psycnet.apa.org/record/1985-18809-001
[18]	Bergman, M. Hearing and aging: Implications of recent research findings. (n.d.) Retrieved October 3, 2023, from www.tandfonline.com/doi/abs/10.3109/00206097109072554
[19]	Winefield, A. Stress in academe: Some recent research findings. (n.d.) Retrieved October 3, 2023, from psycnet.apa.org/record/2001-01125-022
[20]	Toro, P., Dworsky, A., Fowler, P. Homeless youth in the United States: Recent research findings and intervention approaches. (n.d.) Retrieved October 3, 2023, from citeseerx.ist.psu.edu.
[21]	Aslam, F.. (2023). The Benefits and Challenges of Customization within SaaS Cloud Solutions. American Journal of Data, Information and Knowledge Management, 4 (1), 14-22. https://doi.org/10.47672/ajdikm.1543
[22]	Jensen, M., Patterson, D. Hypnotic approaches for chronic pain management: clinical implications of recent research findings. (n.d.) Retrieved October 3, 2023, from psycnet.apa.org/record/2014-04960-006/
[23]	Burstall, C. Factors affecting foreign-language learning: A consideration of some recent research findings. (n.d.) Retrieved October 3, 2023, from www.cambridge.org
[24]	Offer, D., Schonert-Reichl, K. Debunking the myths of adolescence: Findings from recent research. (n.d.) Retrieved October 3, 2023, from www.sciencedirect.com/science/article/pii/S0890856709648097
[25]	Deng, L., Pan, J., Xu, X., Yang, W., Liu, C., Liu, H. PDRLGB: precise DNA-binding residue prediction using a light gradient boosting machine. (n.d.) Retrieved October 3, 2023, from link.springer.com/article/10.1186/s12859-018-2527-1
[26]	Farhan Aslam. (2023). Critical Review of Machine Learning Applications in Cloud ERP Implementations. https://doi.org/10.5281/zenodo.8276406

Cite This Article

Plain Text BibTeX RIS

APA Style

Aslam, F. (2024). Advancing Credit Card Fraud Detection: A Review of Machine Learning Algorithms and the Power of Light Gradient Boosting. American Journal of Computer Science and Technology, 7(1), 9-12. https://doi.org/10.11648/ajcst.20240701.12

Copy | Download

ACS Style

Aslam, F. Advancing Credit Card Fraud Detection: A Review of Machine Learning Algorithms and the Power of Light Gradient Boosting. Am. J. Comput. Sci. Technol. 2024, 7(1), 9-12. doi: 10.11648/ajcst.20240701.12

Copy | Download

AMA Style

Aslam F. Advancing Credit Card Fraud Detection: A Review of Machine Learning Algorithms and the Power of Light Gradient Boosting. Am J Comput Sci Technol. 2024;7(1):9-12. doi: 10.11648/ajcst.20240701.12

Copy | Download

@article{10.11648/ajcst.20240701.12,
  author = {Farhan Aslam},
  title = {Advancing Credit Card Fraud Detection: A Review of Machine Learning Algorithms and the Power of Light Gradient Boosting},
  journal = {American Journal of Computer Science and Technology},
  volume = {7},
  number = {1},
  pages = {9-12},
  doi = {10.11648/ajcst.20240701.12},
  url = {https://doi.org/10.11648/ajcst.20240701.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.ajcst.20240701.12},
  abstract = {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.
},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - Advancing Credit Card Fraud Detection: A Review of Machine Learning Algorithms and the Power of Light Gradient Boosting
AU  - Farhan Aslam
Y1  - 2024/02/01
PY  - 2024
N1  - https://doi.org/10.11648/ajcst.20240701.12
DO  - 10.11648/ajcst.20240701.12
T2  - American Journal of Computer Science and Technology
JF  - American Journal of Computer Science and Technology
JO  - American Journal of Computer Science and Technology
SP  - 9
EP  - 12
PB  - Science Publishing Group
SN  - 2640-012X
UR  - https://doi.org/10.11648/ajcst.20240701.12
AB  - 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.

VL  - 7
IS  - 1
ER  -

Copy | Download

Author Information

Farhan Aslam

Department of Information Technology, University of the Cumberlands, Williamsburg, USA

Contact Email

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Aslam, F. (2024). Advancing Credit Card Fraud Detection: A Review of Machine Learning Algorithms and the Power of Light Gradient Boosting. American Journal of Computer Science and Technology, 7(1), 9-12. https://doi.org/10.11648/ajcst.20240701.12

Copy | Download

ACS Style

Aslam, F. Advancing Credit Card Fraud Detection: A Review of Machine Learning Algorithms and the Power of Light Gradient Boosting. Am. J. Comput. Sci. Technol. 2024, 7(1), 9-12. doi: 10.11648/ajcst.20240701.12

Copy | Download

AMA Style

Aslam F. Advancing Credit Card Fraud Detection: A Review of Machine Learning Algorithms and the Power of Light Gradient Boosting. Am J Comput Sci Technol. 2024;7(1):9-12. doi: 10.11648/ajcst.20240701.12

Copy | Download

@article{10.11648/ajcst.20240701.12,
  author = {Farhan Aslam},
  title = {Advancing Credit Card Fraud Detection: A Review of Machine Learning Algorithms and the Power of Light Gradient Boosting},
  journal = {American Journal of Computer Science and Technology},
  volume = {7},
  number = {1},
  pages = {9-12},
  doi = {10.11648/ajcst.20240701.12},
  url = {https://doi.org/10.11648/ajcst.20240701.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.ajcst.20240701.12},
  abstract = {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.
},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - Advancing Credit Card Fraud Detection: A Review of Machine Learning Algorithms and the Power of Light Gradient Boosting
AU  - Farhan Aslam
Y1  - 2024/02/01
PY  - 2024
N1  - https://doi.org/10.11648/ajcst.20240701.12
DO  - 10.11648/ajcst.20240701.12
T2  - American Journal of Computer Science and Technology
JF  - American Journal of Computer Science and Technology
JO  - American Journal of Computer Science and Technology
SP  - 9
EP  - 12
PB  - Science Publishing Group
SN  - 2640-012X
UR  - https://doi.org/10.11648/ajcst.20240701.12
AB  - 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.

VL  - 7
IS  - 1
ER  -

Copy | Download