A Multi-Layer AI Governance Framework for Enhancing Fraud Detection and Integrity Assurance in Large-Scale Organisational Systems
TL;DR
A multi-layer AI governance framework integrating technical, organizational, and controlling oversight to strengthen fraud detection and integrity assurance in large organizations is proposed and demonstrates scalable and sustainable performance.
Abstract
Large organizations progressively adopt artificial intelligence (AI) to enhance fraud detection and ensure integrity across financial, operational, and digital systems. Despite its benefits, AI introduces challenges including data integrity risks, bias, clarity issues, and accountability gaps. Weak governance can lead to false alerts, model drift, and system vulnerabilities. This research proposes a multi-layer AI governance framework integrating technical, organizational, and controlling oversight to strengthen fraud detection and integrity assurance. The technical layer implements anomaly detection, machine learning, and explainable AI (XAI) models. The organizational layer sets up policies, accountability structures, and increase protocols, while the regulatory layer ensures compliance with GDPR, ISO standards, and AI-specific regulations. The framework adherence interoperability across layers, real-time monitoring, and adaptive responses to progressing fraud patterns. By combining ethical, procedural, and technical safeguards, the study offers a practical, scalable model that improves discovery accuracy, reduces false positives, and enhances organizational resilience and trust in AI systems. This study presents a multi-layer AI management framework that enhances fraud detection and uprightness assurance in large organizations. The framework combines technical, organizational, and regulatory layers to strengthen anomaly identification, audit completeness, and governance maturity. Adaptive thresholds and advanced AI techniques allow real-time detection with decreased alert fatigue, while blockchain and confederated learning ensure robust data integrity. Despite working challenges such as data imbalance, system latency, and explainability concerns, the framework demonstrates scalable and sustainable performance. The study offers actionable insights for policy formulation, stakeholder training, and ethical AI adoption, supporting resilient, accountable, and responsible organizational systems.