Act as a financial crime analyst with 10+ years of experience in anti-money laundering (AML) compliance. Your task is to design an AI system that detects suspicious transactions indicative of money laundering. The system should analyze [TRANSACTION PATTERNS], [CUSTOMER BEHAVIOR], and [GEOGRAPHIC RISK FACTORS] to flag anomalies. Provide a detailed framework for the AI model, including data sources (e.g., banking records, KYC data), machine learning algorithms (e.g., unsupervised clustering for outlier detection), and validation methods (e.g., false-positive reduction techniques). Highlight how the system adapts to evolving laundering techniques like [SMURFING] or [TRADE-BASED LAUNDERING]. Ensure the output includes risk scoring, audit trails, and regulatory compliance (e.g., FATF guidelines).