In the recent past, FinTech firms have more and more adopted Artificial Intelligence (AI) for mission-critical tasks such as credit scoring, fraud detection, and risk evaluation. Black-box models, however, have the enormous challenge of the lack of transparency that renders regulatory transparency under such regulations as General Data Protection Regulation (GDPR), European Banking Authority (EBA) Opinion 2021/01, and the Sarbanes-Oxley Act highly challenging. This paper introduces XAI-Comply, an end-to-end Java Spring Boot microservices system incorporating Explainable AI (XAI) in compliance and reporting processes. SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) services for transaction-level explanation are used, and it employs a novel regulatory-mapping algorithm (Eq. 1) to convert feature-attribution vectors to compliance risk scores and has automated audit-ready report generation using Spring Batch and Apache Kafka. Hosted in Docker containers and orchestrated on Kubernetes, the system is monitored via Prometheus and Grafana. Experimental testing on a simulated dataset demonstrates 70% reduction in compliance exceptions and 60% reduction in report generation delay. Load testing with JMeter confirms consistent throughput of over 500 txn/s. Security is ensured via Vault-based secret management and ISO-27001 compliant patterns. Our contributions are: (1) a microservices architecture for real-time XAI in FinTech. (2) a regulatory-mapping algorithm transforming model-agnostic explanations into actionable metrics; and (3) end-to-end performance and scalability analysis guiding production deployments.