Credit Card Analytics: A Review of Fraud Detection and Risk Assessment Techniques

© 2023 by IJCTT Journal
Volume-71 Issue-10
Year of Publication : 2023
Authors : Kaushikkumar Patel
DOI :  10.14445/22312803/IJCTT-V71I10P109

How to Cite?

Kaushikkumar Patel, "Credit Card Analytics: A Review of Fraud Detection and Risk Assessment Techniques," International Journal of Computer Trends and Technology, vol. 71, no. 10, pp. 69-79, 2023. Crossref,

The financial sector, particularly the credit card industry, has witnessed a transformative shift with the integration of advanced analytics. This review delves into the multifaceted realm of credit card analytics, emphasizing its pivotal role in fraud detection and risk assessment. As digital transactions become ubiquitous, the challenges of ensuring secure and trustworthy transactions have surged. This paper offers a comprehensive overview of the methodologies and techniques employed in detecting credit card fraud, highlighting their effectiveness and limitations. Concurrently, credit risk assessment remains a paramount concern for financial institutions, necessitating robust models that can predict potential defaults and financial losses. The paper further explores the intricacies of data management within the credit card industry, underscoring the importance of high-quality, standardized data for accurate modeling. Challenges in the domain are not sparse; from data inconsistencies to evolving fraud techniques, the industry grapples with numerous obstacles. However, with challenges come solutions. This review proposes several innovative approaches and best practices to navigate these challenges, emphasizing the potential of big data solutions tailored for the financial sector. As the landscape of credit card analytics continues to evolve, this paper also sheds light on potential future research avenues, ensuring that the industry remains at the forefront of technological advancements.

Credit card analytics, Fraud detection, Credit risk assessment, Big data solutions, Financial sector, Predictive modeling, Data management, Machine learning, Regulatory compliance, Transactional security.


[1] Suraj Patil, Varsha Nemade, and Piyush Kumar Soni, “Predictive Modelling for Credit Card Fraud Detection Using Data Analytics,” Procedia Computer Science, vol. 132, pp. 385-395, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Lawrence Borah, S. Saleena, and P. Prakash, “Credit Card Fraud Detection Using Data Mining Techniques,” Journal of Seybold Report, vol. 15, pp. 2431-2436, 2020.
[Google Scholar]
[3] Pooja Tiwari et al., “Credit Card Fraud Detection using Machine Learning: A Study,” arXiv, pp. 1-10, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Pranali Shenvi et al., “Credit Card Fraud Detection using Deep Learning,” IEEE 5th International Conference for Convergence in Technology, Bombay, India, pp. 1-5, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[5] J. Galindo, and P. Tamayo, “Credit Risk Assessment Using Statistical and Machine Learning: Basic Methodology and Risk Modeling Applications,” Computational Economics, vol. 15, pp. 107-143, 2000.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Amol Deshmukh, Customer Segmentation - Credit Cards, Kaggle, 2020. [Online]. Available:
[7] Shubhamoy Dey, “Modeling the Combined Effects of Credit Limit Management and Pricing Actions on Profitability of Credit Card Operations,” International Journal of Business and Management, vol. 5, no. 4, pp. 168-177, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Amir E. Khandani, Adlar J. Kim, and Andrew W. Lo, “Consumer Credit-Risk Models Via Machine-Learning Algorithms,” Journal of Banking and Finance, vol. 34, no. 11, pp. 2767-2787, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Sk. Kamaruddin, and Vadlamani Ravi, “Credit Card Fraud Detection using Big Data Analytics: Use of PSOAANN Based One-Class Classification,” ICIA-16: Proceedings of the International Conference on Informatics and Analytics, pp. 1-8, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[10] N. Skantzos, and N. Castelein, “Credit Scoring Case Study in Data Analytics,” Deloitte, pp. 1-18, 2016.
[Google Scholar] [Publisher Link]
[11] Lewis Alexander et al., “Research Challenges in Financial Data Modeling and Analysis,” Big Data, vol. 5, no. 3, pp. 177-188, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Sadrach Pierre, Mastering Customer Segmentation Using Credit Card Transaction Data, Towards Data Science, 2023. [Online]. Available:
[13] Shantanu Rajora et al., “A Comparative Study of Machine Learning Techniques for Credit Card Fraud Detection Based on Time Variance,” IEEE Symposium Series on Computational Intelligence, pp. 1958-1963, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[14] A. Anand, L. Nario, and M. Banks, Data and Analytics Innovations to Address Emerging Challenges in Credit Portfolio Management, McKinsey and Company, 2022. [Online]. Available:
[15] Credit Risk Analytics and Regulatory Compliance – An Overview, DexLab Analytics, 2015. [Online]. Available:
[16] Artem Chebotko, Andrey Kashlev, and Shiyong Lu, “A Big Data Modeling Methodology for Apache Cassandra,” 2015 IEEE International Congress on Big Data, pp. 238-245, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Varre Perantalu, and K. Bhargav Kiran, “Credit card Fraud Detection Using Predictive Modeling,” International Journal of Innovative Research in Technology, vol. 3, no. 9, pp. 53-58, 2017.
[Google Scholar] [Publisher Link]
[18] Claire Zhang, How to Prepare Data for Credit Risk Modeling, Towards Data Science, 2021. [Online]. Available:
[19] Rebecca Webb, 12 Challenges of Data Analytics and How to Fix them, ClearRisk, 2020. [Online]. Available:
[20] Si Shi et al., “Machine Learning-Driven Credit Risk: A Systematic Review,” Neural Computing and Applications, vol. 34, no. 17, pp. 14327-14339, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Saba Moradi, and Farimah Mokhatab Rafiei, “A Dynamic Credit Risk Assessment Model with Data Mining Techniques: Evidence from Iranian Banks,” Financial Innovation, vol. 5, no. 1, pp. 15, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[22] André Ribeiro, Afonso Silva, and Alberto Rodrigues da Silva, “Data Modeling and Data Analytics: A Survey from a Big Data Perspective,” Journal of Software Engineering and Applications, vol. 8, no. 12, pp. 617-634, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Liyin Jin, Jaimie W. Lien, and Junji Xiao, “Prediction and Learning About Credit Card Spending,” SSRN, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Can Yilmazer, Data Management Steps in Credit Risk Modelling, Finalyse, 2022. [Online]. Available:
[25] Diego Pesco Alcalde, Modelling Credit Card Frauds, Towards Data Science, 2020. [Online]. Available:
[26] Odeajo Israel et al., “Financial Fraud Detection using Machine Learning: Credit Card Fraud,” International Journal of Recent Engineering Science, vol. 10, no. 3, pp. 23-32, 2023.
[CrossRef] [Publisher Link]
[27] Haibing Li et al., “Financial Fraud Detection: Multi-Objective Genetic Programming with Grammars and Statistical Selection Learning,” SSRG International Journal of Computer Science and Engineering, vol. 7, no. 2, pp. 1-18, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Pooja Bhati, and Manoj Sharma, “Credit Card Number Fraud Detection Using K-Means with Hidden Markov Method,” SSRG International Journal of Mobile Computing and Application, vol. 2, no. 2, pp. 15-18, 2015.
[CrossRef] [Google Scholar] [Publisher Link]