Predictive Analytics Models for Financial Risk Detection and Fraud Prevention in Public Systems

Predictive analytics models are increasingly critical for strengthening financial risk detection and fraud prevention in public systems characterized by complex transactions, fragmented oversight, and high exposure to leakage. This study proposes a comprehensive predictive analytics framework designed to enhance transparency, accountability, and early warning capabilities across government financial management platforms. The research integrates machine learning algorithms, anomaly detection techniques, and network-based risk scoring models to identify irregular patterns in procurement, payroll, grants administration, and revenue collection processes. Using structured and unstructured data streams from enterprise resource planning systems, audit logs, and transactional databases, the framework applies supervised and unsupervised learning methods including logistic regression, random forests, gradient boosting, and graph analytics to detect suspicious activities in near real time. The model architecture incorporates data preprocessing pipelines, feature engineering strategies, and adaptive threshold mechanisms to reduce false positives while improving detection sensitivity. A hybrid ensemble approach is introduced to enhance predictive accuracy and model robustness across heterogeneous public sector datasets. Scenario-based simulations and retrospective case validation demonstrate improved fraud detection rates, reduced financial loss exposure, and faster response cycles compared to traditional rule-based control systems. Beyond detection, the framework embeds explainable artificial intelligence components to ensure interpretability, regulatory compliance, and stakeholder trust. Risk dashboards and automated alerts are designed to support decision-makers in audit institutions, anti-corruption agencies, and treasury departments. The study further examines governance implications, data privacy considerations, and ethical safeguards necessary for responsible AI deployment in public financial oversight. Findings indicate that predictive analytics significantly enhances proactive risk mitigation, strengthens internal control environments, and contributes to institutional resilience. The proposed model offers a scalable and interoperable solution adaptable to diverse public finance ecosystems, particularly in emerging economies facing systemic accountability challenges. By combining advanced analytics with governance-centric design principles, this research advances the discourse on technology-enabled financial integrity and sustainable public resource management.