Credit Card Analytics: A Review of Fraud Detection and Risk Assessment Techniques |
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| © 2023 by IJCTT Journal | ||
| Volume-71 Issue-10 |
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| 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, https://doi.org/10.14445/22312803/IJCTT-V71I10P109
Abstract
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.
Keywords
Credit card analytics, Fraud detection, Credit risk assessment, Big data solutions, Financial sector, Predictive modeling, Data management, Machine learning, Regulatory compliance, Transactional security.
Reference
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