Anomaly detection is an important problem in data science on account of its wide applications across various fields. However, existing approaches often challenge due to lack of interpretability in the detected anomalies. In this study, we introduce a novel improvement to the traditional isolation forest by integrating it with decision trees, leading to a technique that has both high fidelity and explainability. This hybrid approach makes the isolation forest transparent by generating ‘if–then’ rules utilizing the decision tree. Thorough experiments conducted on four banking and finance datasets demonstrate that our approach not only achieves a high detection performance and fidelity but also provides explanation to the isolation forest.