Fraud Detection in Banking and Finance: A Multi-Layered Approach using Velocity, Identity, and Location Intelligence

Fraud in banking and finance has grown in scale and sophistication with the rise of digital payments and remote onboarding. Traditional rule-based systems alone struggle to counter evolving threats such as synthetic identities, account takeovers, and geo-spoofing. This paper proposes a multi-layered fraud detection framework that integrates velocity and geo-velocity checks, device fingerprinting, behavioral analytics, identity verification, and email/phone intelligence. Through literature review and case studies, the study demonstrates how hybrid approaches combining supervised and unsupervised machine learning with real-time rules can improve detection accuracy, reduce false positives, and preserve customer experience. The findings highlight the importance of layered defenses, privacy- preserving collaboration, and adaptive AI models in addressing modern fraud challenges