Optimizing Payment Risk with Machine Learning |
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| © 2025 by IJCTT Journal | ||
| Volume-73 Issue-1 |
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| Year of Publication : 2025 | ||
| Authors : Prerna Kaul, Abhai Pratap Singh | ||
| DOI : 10.14445/22312803/IJCTT-V73I1P115 | ||
How to Cite?
Prerna Kaul, Abhai Pratap Singh, "Optimizing Payment Risk with Machine Learning," International Journal of Computer Trends and Technology, vol. 73, no. 1, pp. 128-135, 2025. Crossref, https://doi.org/10.14445/22312803/IJCTT-V73I1P115
Abstract
Modern payment systems have become increasingly complex and have attracted various challenges, such as fraud risk, transaction failures, and regulatory compliance requirements. This article will discuss Machine Learning (ML) solutions that could adequately solve these problems. This study introduces two approaches: a payment risk detection model that mitigates the impact of fraudulent transactions before they happen and an alternative payment optimization model that recovers failed payments in a customer-friendly way. These systems provide considerable cost savings, improved user experiences, and regulatory compliance. It details the technical aspects, the measurable results, and the future paths available to do so, inspiring other businesses to modernize the payment experience through machine learning.
Keywords
Fraud risk management, Machine Learning (ML), Payment risk detection model, Transaction failures, User Experience.
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