Towards transparent financial AI: a systematic review of graph learning and explainable methods for credit risk and fraud detection

Abstract

Graph-based learning and explainable artificial intelligence (XAI) are increasingly used to improve both predictive performance and transparency in financial risk modelling. This paper presents a systematic literature review of AI and machine learning approaches for credit risk assessment and fraud detection, with specific attention to graph-based methods and explainable frameworks. Following a PRISMA-guided methodology, 149 studies published between 2015 and 2025 were analysed across multiple academic databases. The review identifies three key findings. First, graph-based models, particularly graph neural networks, can improve the modelling of relational dependencies in financial data, although their use remains more developed in fraud detection than in credit risk assessment. Second, XAI techniques such as SHAP, LIME, and rule-based methods are increasingly used to support interpretability, auditability, and regulatory compliance, but their integration with graph-based models remains limited. Third, recent research is shifting from purely predictive modelling towards deployment-oriented financial AI systems that address class imbalance, concept drift, scalability, real-time monitoring, and governance. To address these gaps, this paper proposes a deployment-oriented reference framework that links graph construction, graph-based modelling, explainability mechanisms, monitoring, human oversight, and governance controls. The findings provide a structured synthesis of current research and practical guidance for developing scalable, transparent, and accountable financial AI systems.