The pervasive issue of fraudulent transactions presents a considerable challenge for financial institutions globally. Developing innovative fraud detection systems is critical to maintaining customer confidence. However, several factors complicate the creating of effective and efficient fraud detection systems. Notably, fraudulent transactions are infrequent, resulting in imbalanced transaction datasets where legitimate transactions vastly outnumber instances of fraud. This data imbalance can concede the performance of fraud detection. Additionally, stringent data privacy regulations prevent the sharing of customer data, hindering the development of high-performing centralized models. Furthermore, fraud detection mechanisms must remain transparent to avoid impairing the user experience. This research proposes an approach utilizing Federated Learning (FL) with Explainable Artificial Intelligence (XAI) to overcome these obstacles. FL allows financial organizations to train fraud detection models collaboratively without requiring direct data sharing. So, customer confidentiality and data privacy are never compromised. Simultaneously, the incorporation of XAI guarantees that the model’s predictions are interpretable by human experts. Experimental evaluations using real-time transaction datasets consistently demonstrate that the FL-based fraud detection system performs well. This study establishes the potential of FL as a reliable, privacy-preserving tool in combating fraud.