<p>As complex black-box Artificial Intelligence (AI) models become integral to high-stakes domains like fraud detection, the need for transparency is becoming critical. Explainable AI (XAI) aims to make algorithmic decisions understandable to stakeholders. While systematic reviews have mapped the use of XAI in the broader financial sector, a focused synthesis of the methodological challenges unique to fraud detection remains a critical gap. This systematic literature review addresses this gap, following PRISMA 2020 guidance and synthesizing 49 peer-reviewed studies (2021–2025) to identify current practices and foundational challenges. Our analysis reveals that while post-hoc methods like SHAP and LIME are prevalent, their application is undermined by two systemic methodological flaws that threaten the validity of current research. First, we identify an explainability–imbalance paradox, where common data resampling techniques used to manage class imbalance can distort background distributions or local neighborhoods and thereby compromise the faithfulness and fidelity of post-hoc explanations. Second, we discover a profound evaluation vacuum, with around 80% of the analyzed studies using model predictive performance as a proxy for explanation quality rather than directly evaluating the explanations themselves. We also critically assess when post-hoc explanations are appropriate versus when intrinsically interpretable models are preferable in auditable and high-risk contexts. Based on these findings, we propose an explanation-aware training and evaluation checklist and a research agenda to guide the field toward standardized, explanation-level evaluation and the development of imbalance-robust, explanation-preserving data-processing methods.</p>