Explainable AI in Insider Financial Fraud Detection Models: A Review of Transparency and Trust

Abstract

Financial and insider fraud increasingly intersect with broader cybercrime ecosystems, creating attack vectors that undermine national cyber resilience and the integrity of digital financial infrastructures. As organizations turn to machine learning (ML) and deep learning (DL) models for automated fraud and insider-threat detection, the opacity of these systems presents strategic risks for cyber defense: unexplainable alerts weaken analyst trust, complicate incident response, and challenge regulatory and forensic accountability. This study presents a systematic review of 107 empirically validated works (2015–2025) examining how Explainable Artificial Intelligence (XAI) techniques enhance transparency, trustworthiness, and operational readiness in AI-driven fraud detection systems. Using a mixed bibliometric–thematic methodology, the review maps the evolution of ML/DL architectures, XAI adoption patterns, evaluation practices, and dataset limitations within security-critical environments. The findings highlight a sector-wide dependence on post-hoc feature attribution and reveal emerging shifts toward intrinsic interpretability through attention mechanisms and hybrid temporal models. Despite progress, gaps persist: limited use of sequential behavioral models, narrow evaluation metrics, and overreliance on structured datasets weaken real-world resilience against adaptive adversaries. To address these challenges, the paper proposes a Three-Pillar Framework: Algorithmic Transparency, Evaluation Accountability, and Data Traceability that positions explainability as a foundational architectural property for cyber defense systems. By aligning model interpretability with security operations, regulatory requirements, and analyst cognition, the framework strengthens organizational readiness against insider threats, financial fraud, and AI-targeted adversarial manipulation, key considerations in modern cyber warfare and security operations.