Abstract

A Review of VoIP Forensic Analytics Models for Financial Fraud Detection and Regulatory Compliance Monitoring examines the evolving role of voice over Internet Protocol data as a critical evidential and intelligence source in modern financial ecosystems. As banking, fintech, and digital payment platforms increasingly rely on cloud telephony and unified communications, voice traffic has become a valuable channel for detecting fraud, insider threats, and compliance breaches. However, fragmented analytical models and inconsistent regulatory integration limit the effectiveness of VoIP forensic capabilities across jurisdictions and institutions. This review synthesizes existing research on VoIP traffic analysis, speaker profiling, call pattern mining, anomaly detection, and metadata correlation. It evaluates how machine learning, deep learning, and graph analytics are applied to identify suspicious communication patterns and support regulatory monitoring. The paper categorizes models into signature-based detection, behavioral analytics, and predictive risk scoring approaches. Key strengths, limitations, and implementation challenges are analyzed, including scalability, privacy concerns, encryption barriers, and cross border data governance requirements. The review highlights the growing alignment between VoIP forensics and regulatory frameworks such as anti-money laundering, know your customer, and market conduct surveillance obligations. It proposes an integrated analytics lifecycle that combines data acquisition, preprocessing, feature engineering, model training, and audit ready reporting. Emphasis is placed on explainable artificial intelligence and automated evidence generation to support investigations and legal defensibility. The study also discusses the role of real time monitoring dashboards and risk scoring engines in strengthening proactive compliance. By consolidating fragmented knowledge, the review provides a comprehensive foundation for financial institutions seeking to enhance fraud detection and regulatory assurance. It identifies research gaps related to dataset availability, standard benchmarking, and integration with enterprise governance platforms. Future directions include hybrid analytics, privacy preserving computation, and cross industry collaboration. The findings contribute practical insights for investigators, regulators, and technology developers aiming to build resilient communication surveillance capabilities. Overall, the review positions VoIP forensic analytics as a strategic component of digital trust and financial system integrity in an increasingly connected world. This perspective encourages standardized frameworks, shared datasets, and multidisciplinary governance to accelerate adoption and measurable compliance outcomes globally across sectors.