The field of symbolic regression is witnessing a significant shift towards enhancing both performance and explainability, particularly in high-stakes applications such as financial fraud detection. Researchers are increasingly integrating fuzzy logic and neuro-symbolic AI to address the inherent complexity and uncertainty in these domains. The adoption of similarity-based approaches and the introduction of novel search objectives, such as the minimum description length, are advancing the accuracy and recovery rates of symbolic regression models. Additionally, the use of advanced AI techniques like GPT-guided Monte Carlo Tree Search is enabling faster and more transparent decision-making processes. These innovations not only improve the efficacy of fraud detection but also provide valuable insights into the decision-making mechanisms, thereby bridging the gap between performance and explainability. Notably, the integration of fuzzy logic into Deep Symbolic Regression and the development of GPT-guided MCTS for symbolic regression in financial fraud detection stand out for their potential to significantly advance the field.