Generative Intelligence for Fraud-Proof Wealth and Compliance
Keywords:
Generative Financial Intelligence, Fraud-Resistant Wealth Management, Financial Fraud Detection, Regulatory Compliance, Generative Artificial Intelligence, Anti-Money Laundering, Financial Risk Analytics, Intelligent Decision Support, Wealth Management Analytics, Compliance Intelligence.Abstract
This work presents a Generative Financial Intelligence Framework designed to strengthen fraud resistance in wealth management and detect compliance violations related to Anti-Money Laundering (AML) and Counter Financing of Terrorism (CFT) obligations. The framework comprises four core components: (1) data architecture, (2) anomaly detection and risk scoring, (3) fraud-resistant wealth management, and (4) synthetic data and scenario generation. Each component is grounded in Principal-Agent Theory and designed to comply with relevant financial regulations, governance standards, and supervisory expectations.
Generative Financial Intelligence is defined here as an enterprise’s capability to generate auxiliary, data-driven models from internal and external knowledge sources to support decision-making. These models serve dual purposes: understanding and improving enterprise operations, and predicting future behaviors of the enterprise or its stakeholders. This concept extends beyond the definition of Financial Intelligence established by the Association of Certified Financial Crime Specialists (ACFCS), by augmenting predictive capabilities derived from internal data with auxiliary data sourced from both private-sector and public-domain channels.
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Copyright (c) 2025 Venkata krishna azith ganti, Luca Romano (Author)
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