Predictive Analytics for Transaction Failures in Payment Gateways
Keywords:
Predictive Analytics, Transaction Failures, Payment Gateways, Machine Learning, Data Mining.Abstract
The era of digital payments has transformed the global economy, but for businesses and consumers, transaction failures on payment gateways remain the biggest inconvenience to be solved. These failures are not only obstacles against the effectiveness for e-commerce activities, but also harm the trust and satisfaction of users. Predictive analytics provide a potential means to address the problem, using sophisticated methods to predict transaction failures in advance. In this paper considered the impact of predictive analytics on predicting failed transactions in online payment gateways focusing on machine learning algorithms, data mining, and artificial intelligence. Based on a thorough investigation of the available methods and predictive models, this study shows an opportunity to use the real-time analysis of data to identify patterns and deviations from normal in transaction processing. Inclusion of predictive analytics in payment gateway systems allows tracking of risk factors, decisions, and strategy and pro-active intervention to avoid failures. Through greater transaction reliability, lower downtime and enhanced security levels, predictive analytics has a significant part to play in ensuring the stability and sustainability of digital payment infrastructures. The paper goes on to examine the viability of different algorithms, with a view to determine their accuracy and relevance to payment systems challenges. The purpose of this study is to gain a better understanding of the possible outcome that will result from the application of predictive analytics in the future of digital payments, guiding payment gateways providers and researchers.
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References
Patel, L., & Li, M. (2021). Customer Segmentation Using Predictive Analytics in Banking. International Journal of Bank Marketing, 39(4), 652-670.
Johnson, K., & Williams, D. (2021). Ethics of AI in Finance: Navigating Bias and Transparency. Journal of Business Ethics, 169(2), 345-360.
Zhao, X., & Chen, Y. (2021). Real-Time Data Analytics in Payment Systems: Opportunities and Challenges. Journal of Financial Technology and Innovation, 6(1), 78-92.
European Union. (2016). General Data Protection Regulation (GDPR). Retrieved from https://gdpr-info.eu/
A. D. Pozzolo, G. Boracchi, O. Caelen, C. Alippi and G. Bontempi, "Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy," in IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 8, pp. 3784-3797, Aug. 2018, doi: 10.1109/TNNLS.2017.2736643. https://ieeexplore.ieee.org/document/8038008
MarketsandMarkets, "Digital Payment Market by Component (Solutions (Payment Processing, Payment Gateway, Payment Wallet, POS Solution, Payment Security and Fraud Management) and Services), Deployment Mode, Organization Size, Vertical and Region - Global Forecast to 2026," MarketsandMarkets, Jul. 2021
R. K. Gupta and M. R. Jain, "Fraud Detection in Financial Transactions Using Machine Learning Techniques," Journal of Computer and Communications, vol. 8, no. 2, pp. 19-27, 2020.
Z. B. Alzahrani, A. A. Alzahrani, and M. L. Yaakob, "Optimizing Payment Gateways: A Machine Learning Approach," IEEE Access, vol. 8, pp. 110004-110017, 2020.
M. R. Choudhury and R. H. Uddin, "Machine Learning Techniques for Predicting Credit Card Fraud: A Comparative Study," International Journal of Information Technology, vol. 12, pp. 35-50, 2020.
S. M. Rahman, N. Ahmed, and M. S. Rahman, "Anomaly Detection in Financial Transactions Using Machine Learning," International Journal of Computer Applications, vol. 975, no. 8895, pp. 12-18, 2019.
P. B. Gohil and S. C. Patel, "Payment Gateway Security: A Review," International Journal of Computer Science and Information Security, vol. 18, no. 6, pp. 15-20, 2020.
K. A. Anwar and R. A. Khan, "Real-Time Fraud Detection System Using Machine Learning," International Journal of Advanced Research in Computer Science and Software Engineering, vol. 7, no. 5, pp. 50-56, 2017.
M. A. Alzahrani, "Machine Learning for Financial Applications: Opportunities and Challenges," IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 5, pp. 2047-2060, 2021.
F. T. Rosset and L. D. V. Soares, "A Comprehensive Survey of Machine Learning Applications in Financial Technology," Expert Systems with Applications, vol. 140, pp. 112868, 2020.
N. M. Ahmed, J. H. T. Hossain, and S. A. Rahman, "Machine Learning Algorithms for Financial Fraud Detection: A Survey," Journal of Finance and Data Science, vol. 6, no. 3, pp. 205-216, 2020.
G. A. Jayathilake, "The Role of Machine Learning in Payment Processing Systems: A Literature Review," Artificial Intelligence Review, vol. 53, no. 3, pp. 2001-2023, 2020.
K. S. Shariati and K. R. Cheung, "An Intelligent Payment Gateway Using Machine Learning," Journal of Computer Networks and Communications, vol. 2020, pp. 1-10, 2020.
R. N. Shafique and J. R. B. U. Rahman, "Recent Advances in Machine Learning for Payment Processing," IEEE Access, vol. 9, pp. 112243-112258, 2021.
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