Machine Learning Models for Multi Sector Fraud Detection in Public and Private Financial Systems

Fraud detection is a critical challenge across public and private financial systems due to increasing transaction volumes, evolving adversarial behavior, and strict regulatory constraints. While machine learning has significantly improved fraud detection performance, existing research largely focuses on single-sector applications, limiting its effectiveness in addressing cross-sector fraud patterns. This paper presents a comprehensive review of machine learning models for fraud detection in multi-sector financial systems, covering banking, insurance, and public-sector domains. We examine supervised, unsupervised, semi-supervised, deep learning, and graph-based approaches, highlighting their strengths and limitations in real-world settings. In addition, we analyze key challenges related to data imbalance, label uncertainty, evaluation metrics, scalability, privacy, and explainability. Finally, the paper identifies open research directions, including cross-sector generalization, privacy-preserving collaboration, scalable graph learning, and continual adaptation to evolving fraud strategies. This review provides a structured reference for researchers and practitioners seeking to design effective, responsible, and scalable fraud detection systems.