The increasing sophistication of fraud has rendered rule-based fraud detection obsolete, exposing banks to greater financial risk, reputational damage, and regulatory penalties. Financial stability, customer trust, and compliance are increasingly threatened as centralized Artificial Intelligence (AI) models fail to adapt, leading to inefficiencies, false positives, and undetected detection. These limitations necessitate advanced AI solutions for banks to adapt properly to emerging fraud patterns. While AI enhances fraud detection, its black-box nature limits transparency, making it difficult for analysts to trust, validate, and refine decisions, posing challenges for compliance, fraud explanation, and adversarial defense. Effective fraud detection requires models that balance high accuracy and adaptability to emerging fraud patterns. Federated Learning (FL) enables distributed training for fraud detection while preserving data privacy and ensuring legal compliance. However, traditional FL approaches operate as black-box systems, limiting the analysts to trust, verify, or even improve the decisions made by AI in fraud detection. Explainable AI (XAI) enhances fraud analysis by improving interpretability, fostering trust, refining classifications, and ensuring compliance. The integration of XAI and FL forms a privacy-preserving and explainable model that enhances security and decision-making. This research proposes an Explainable FL (XFL) model for financial fraud detection, addressing both FL’s security and XAI’s interpretability. With the help of Shapley Additive Explanations (SHAP) and LIME, analysts can explain and improve fraud classification while maintaining privacy, accuracy, and compliance. The proposed model is trained on a financial fraud detection dataset, and the results highlight the efficiency of detection and successful elimination of false positives and contribute to the improvement of the existing models as the proposed model attained 99.95% accuracy and a miss rate of 0.05%, paving the way for a more effective and comprehensive AI-based system to detect potential fraudulence in banking.

Background: Industry 4.0’s development requires digitalized manufacturing through Predictive Maintenance (PdM) because such practices decrease equipment failures and operational disruptions. However, its effectiveness is hindered by three key challenges: (1) data confidentiality, as traditional methods rely on centralized data sharing, raising concerns about security and regulatory compliance; (2) a lack of interpretability, where opaque AI models provide limited transparency, making it difficult for operators to trust and act on failure predictions; and (3) adaptability issues, as many existing solutions struggle to maintain a consistent performance across diverse industrial environments. Addressing these challenges requires a privacy-preserving, interpretable, and adaptive Artificial Intelligence (AI) model that ensures secure, reliable, and transparent PdM while meeting industry standards and regulatory requirements. Methods: Explainable AI (XAI) plays a crucial role in enhancing transparency and trust in PdM models by providing interpretable insights into failure predictions. Meanwhile, Federated Learning (FL) ensures privacy-preserving, decentralized model training, allowing multiple industrial sites to collaborate without sharing sensitive operational data. This proposed research developed a sustainable privacy-preserving Explainable FL (XFL) model that integrates XAI techniques like Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) into an FL structure to improve PdM’s security and interpretability capabilities. Results: The proposed XFL model enables industrial operators to interpret, validate, and refine AI-driven maintenance strategies while ensuring data privacy, accuracy, and regulatory compliance. Conclusions: This model significantly improves failure prediction, reduces unplanned downtime, and strengthens trust in AI-driven decision-making. The simulation results confirm its high reliability, achieving 98.15% accuracy with a minimal 1.85% miss rate, demonstrating its effectiveness as a scalable, secure, and interpretable solution for PdM in Industry 4.0.