Scholarlink

This research discusses the combination of the “federated learning and blockchain technology to optimize fraud detection” in the cross-border transactions. With the increase in digital payment systems across the world, the need for safe and privacy-involving solutions rises to the fore. The federal learning does not share the sensitive data but is able to train the machine learning models in multiple organizations at the same time which is a novel approach incorporating in fraud detection by the nature of blockchain which is decentralized and immutable. Four Machine Learning algorithms, namely, XGBoost, CatBoost, Random Forest and Logistic Regression were attempted and assessed in terms of their performance in a Federated Learning scenario. “The results of the experiment showed that the federated learning was superior to the traditional learning, which demonstrated the accuracy of 94.7%, precision of 92.4%, and recall of 91.8%. Meanwhile, XGBoost and CatBoost had the best results with the value of 92.6% and 91.5% respectively, outperforming Random Forest and Logistic Regression”. From the study, it is evident that the federated learning can be able protect the privacy and scalability while being able to detect fraud accurately on the decentralized systems. These findings are an addition to the emerging research on cross-border payment security and building blocks for future research in the use of advanced machine learning and blockchain for digital finance fraud prevention.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Next-Gen Payment Gateways: Leveraging Federated Learning for Fraud Detection in Cross-Border Transactions

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Srinivas Kalisetty,
  • Phanish Lakkarasu,
  • Sneha Singireddy,
  • Jai Kiran Reddy Burugulla,
  • Kishore Challa,
  • Anil Lokesh Gadi

摘要

This research discusses the combination of the “federated learning and blockchain technology to optimize fraud detection” in the cross-border transactions. With the increase in digital payment systems across the world, the need for safe and privacy-involving solutions rises to the fore. The federal learning does not share the sensitive data but is able to train the machine learning models in multiple organizations at the same time which is a novel approach incorporating in fraud detection by the nature of blockchain which is decentralized and immutable. Four Machine Learning algorithms, namely, XGBoost, CatBoost, Random Forest and Logistic Regression were attempted and assessed in terms of their performance in a Federated Learning scenario. “The results of the experiment showed that the federated learning was superior to the traditional learning, which demonstrated the accuracy of 94.7%, precision of 92.4%, and recall of 91.8%. Meanwhile, XGBoost and CatBoost had the best results with the value of 92.6% and 91.5% respectively, outperforming Random Forest and Logistic Regression”. From the study, it is evident that the federated learning can be able protect the privacy and scalability while being able to detect fraud accurately on the decentralized systems. These findings are an addition to the emerging research on cross-border payment security and building blocks for future research in the use of advanced machine learning and blockchain for digital finance fraud prevention.