Geographical Visibility & Control Architecture for Credit and Debit Cards Using AI with Appropriate Consumer Data Protection

Abstract

This paper proposes an AI-driven architecture to enhance real-time geographical visibility and control over credit and debit card transactions. The system integrates advanced machine learning algorithms for fraud detection, dynamic location-based transaction authorization, and robust data protection mechanisms to safeguard consumer privacy and ensure regulatory compliance.

Introduction

The widespread use of credit and debit cards has increased fraud risks, necessitating enhanced security measures. This paper proposes an AI-powered system that incorporates real-time geographical transaction monitoring combined with consumer and issuer inputs to tailor location-based transaction controls while ensuring data privacy.

Key Components:

• Transaction Monitoring Engine: Captures transaction and geographic data.

• Location Verification Module: Validates transactions against allowed and blocked regions set by consumers and issuers.

• AI Fraud Detection Engine: Uses machine learning models to calculate fraud probability from transaction and location features.

• Consumer Data Protection Layer: Applies encryption, anonymization, and access controls complying with privacy regulations like GDPR.

System Features:

• Consumers specify allowed regions for card use.

• Issuers provide high-risk or blocked regions based on fraud trends.

• Transactions are evaluated for geographic deviation using a haversine distance formula.

• An ensemble AI model calculates fraud probability.

• Combined risk score balances location risk and AI fraud prediction.

• Transactions are approved, declined, or flagged for multi-factor authentication based on this score.

Privacy Measures:

• Encryption of data at rest and in transit.

• Use of anonymization techniques such as k-anonymity and differential privacy.

• Consumer consent protocols and legal compliance.

Results:

Integrating AI with geographical control improves fraud detection accuracy, reduces false positives, and enhances user experience by customizing transaction approvals based on location and behavior patterns.

Conclusion

This paper presents a novel AI-driven architecture combining geographical visibility and control for credit and debit card transactions with data protection mechanisms. The inclusion of dynamic consumer and issuer inputs personalizes regional access control, improving fraud prevention without compromising consumer privacy or convenience.

References

[1] Lodha, K., Zargar, K. S., \"AI-Powered Detection of Financial Deception: Uncovering Credit Card Fraud,\" Int. J. Computer Applications, vol. 187, no. 13, pp. 39-46, 2025. [2] \"An Ensemble Machine Learning Approach for Enhancing Credit Card Fraud Detection,\" J. Neonatal Surgery, 2025. [3] PCI Security Standards Council, \"Payment Card Industry Data Security Standard,\" 2024. [4] GDPR, \"General Data Protection Regulation,\" European Union, 2018.

Copyright

Copyright © 2025 Vijayakumar Radhakrishnan. 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.