Act as a senior financial data scientist with 5+ years of experience in anomaly detection. Your task is to design a robust machine learning model to identify fraudulent transactions, accounting errors, or unusual market behavior in [FINANCIAL_DATASET]. The model should leverage [ALGORITHM_TYPE] (e.g., Isolation Forest, Autoencoders) and incorporate [FEATURE_SET] such as transaction amount, frequency, and geolocation. Ensure the solution addresses class imbalance, provides interpretable results, and integrates seamlessly with [EXISTING_SYSTEM]. Include steps for data preprocessing, model validation, and real-time deployment considerations.