SmartRingGuard+: A Liveness-Aware Multimodal Wearable Authentication Framework for Credit Card Fraud Prevention

Abstract

Credit card fraud remains a significant challenge in modern digital payment systems, causing substantial financial losses for financial institutions and consumers. Traditional authentication mechanisms such as PINs, passwords, and one-time passwords often provide limited protection against advanced threats including card theft, cloning, and social engineering attacks. This study presents SmartRingGuard+, a liveness-aware multimodal wearable authentication framework designed to improve credit card transaction security. The system employs a smart wearable ring that integrates biometric sensing, gesture-based confirmation, and physiological liveness verification to ensure the presence of the legitimate cardholder during transactions. Additionally, the framework incorporates behavioral spending analysis and contextual risk assessment to detect suspicious transaction patterns. A machine learning–based fraud risk model analyzes multiple signals to determine transaction authenticity. Experimental evaluation using publicly available credit card transaction datasets indicates that the proposed approach improves fraud detection performance compared with traditional single-factor authentication mechanisms. The SmartRingGuard+ framework provides a scalable and user-friendly solution for enhancing security in modern digital payment ecosystems.

Introduction

The text discusses the growing problem of credit card fraud in the digital payment era, where traditional authentication methods like PINs, passwords, and OTPs are increasingly vulnerable to cyberattacks such as phishing and identity theft. Although machine learning has improved fraud detection by analyzing transaction patterns, it often fails to verify whether the actual cardholder is present during a transaction.

To address these limitations, the proposed system, SmartRingGuard+, introduces a multimodal wearable authentication framework using a smart ring. This system combines multiple layers of security, including biometric verification (e.g., heart rate and motion), gesture-based confirmation, and physiological liveness detection to ensure that the user is physically present and genuine. It also incorporates behavioral transaction analysis and contextual data, evaluated through a machine learning model to assess fraud risk.

The framework works by collecting real-time data from the wearable device during a transaction and comparing it with stored user profiles and spending patterns. Based on this analysis, the system decides whether to approve, reject, or further verify the transaction.

Overall, SmartRingGuard+ enhances credit card security by integrating wearable technology, biometrics, and intelligent fraud detection, offering a more robust and user-friendly alternative to traditional authentication methods.

Conclusion

This paper presented SmartRingGuard+, a liveness-aware multimodal wearable authentication framework designed to enhance credit card fraud prevention. The proposed framework integrates multiple authentication mechanisms including wearable biometric verification, gesture-based confirmation, physiological liveness detection, and behavioral transaction analysis. By combining these authentication signals with machine learning–based fraud detection, the framework provides a robust mechanism for identifying potentially fraudulent transactions while maintaining a seamless user experience for legitimate users. The experimental evaluation was conducted using a publicly available credit card fraud dataset. Several machine learning algorithms were tested to evaluate the effectiveness of the proposed framework. Among the evaluated models, the Random Forest classifier achieved the best performance, demonstrating high accuracy and improved fraud detection capability. The results also show that integrating wearable authentication signals with behavioral transaction analysis significantly enhances the ability to detect fraudulent activities compared with traditional transaction-based fraud detection methods. The findings suggest that wearable authentication technologies can provide an additional layer of security for digital payment systems. By verifying the physical presence of the legitimate cardholder and analyzing behavioral transaction patterns, the SmartRingGuard+ framework can effectively reduce the risk of unauthorized transactions. Future research will focus on implementing the proposed framework using real-world wearable devices and evaluating its performance using live biometric sensor data. Additional improvements may include incorporating deep learning techniques for fraud detection, enhancing gesture recognition accuracy, and extending the framework to support other types of digital payment platforms such as mobile wallets and contactless payment systems.

References

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Copyright

Copyright © 2026 Pawan Mohite, Sumeet Sable , Pruthviraj Gavhane , Tanmay Patil , Prof. Prranjali Jadhav, Prof. Yashwant Ingle. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.