Dynamic Location-Based Transaction Limits for Enhanced Fraud Prevention in Financial Services

Abstract

This paper presents a novel, dynamic location-based transaction limit system designed to enhance financial fraud prevention by adapting security measures in real-time. Traditional static fraud prevention methods are increasingly ineffective in dynamic, high-risk environments, particularly against geolocation spoofing. Our proposed system addresses this by integrating multi-source geolocation verification, cryptographic checksums, and digital signatures to ensure location data integrity. Utilizing machine learning, the system dynamically classifies locations into risk zones (high, medium, low) and adjusts transaction limits accordingly. Performance evaluations demonstrate over 90% accuracy in risk assessment, a 30% reduction in fraud incidents compared to static methods, and an average response time of 300 milliseconds. Notably, the system reduces false-positive alerts by 25%, significantly improving user experience. This research contributes a scalable and adaptable solution for location-sensitive financial security, with future work focusing on advanced predictive analytics for risk zone refinement and personalized risk adjustments based on user behavior.