Detecting abnormal subgraphs is crucial for revealing illicit activities hidden within complex financial networks. Existing work lacks system-level tools for interactively understanding detected anomalies. This limitation hampers their applicability in real-world financial analysis. To address this issue, we present an Automated Anomalous Subgraph Discovery (AutoASD) system, which automatically identifies and analyzes anomalous subgraph structures for effective discovery. Our interactive interface allows users to explore detailed abnormal subgraphs and gain real-time insights related to key metrics such as abnormality degree. By offering visual comparisons across multiple advanced methods, the system enhances interpretability and provides practical support for investigating potential money-laundering activities such as smurf-like layering patterns. This demo enables more intuitive understanding and informed decision-making in financial data analysis. The demonstration video is available at https://github.com/Killian-Lee/AutoASD .