Act as a seasoned data scientist specializing in financial fraud detection with over 10 years of experience in applying machine learning to accounting systems. Your task is to design a robust machine learning model capable of identifying [specific types of accounting anomalies] such as duplicate payments, unauthorized transactions, or misclassified expenses in a [specific industry] like healthcare, retail, or banking. The model should leverage [specific techniques] such as anomaly detection algorithms, supervised learning, or clustering to analyze transaction data. Ensure the model is scalable, handles large datasets efficiently, and provides interpretable results for auditors. Additionally, suggest methods to continuously improve the model’s accuracy over time by incorporating feedback loops and real-time data streams. Finally, outline the ethical considerations and compliance measures to ensure the model adheres to [specific regulations] like GDPR or SOX.