Generative AI Framework for Wealth Fraud Detection & Compliance Risk
Keywords:
Generative Financial Intelligence, Financial Intelligence Framework, Fraud-Aware Analytics, Financial Fraud Detection, Predictive Financial Analytics, Risk Signal Detection, Wealth Intelligence, Compliance Analytics, Explainable Artificial Intelligence, Decision Intelligence, Financial Risk Management, Regulatory Technology (RegTech).Abstract
The Financial Intelligence Framework is grounded in the epistemic and methodological foundations of Generative Financial Intelligence, distinguishing predictive analytics from a broader paradigm capable of addressing complex, adaptive, and undocumented problems while emphasizing fraud-aware analytics. Predictive financial signal detection focuses narrowly on solving forecasting tasks at low total cost of ownership, supporting risk signal detection, control, and decision activation. Generative Financial Intelligence extends this scope beyond risk detection to encompass profiling, waypointing, advisory functions, prescription, and debiasing, making it well-suited to adaptive domains such as fraud detection and prevention. Within these domains, the framework enables the classification of fraud types and construction of taxonomies, the delineation of indicators, and their linkage to signal detection, control mechanisms, and detection-threshold decisions—culminating in the automation and formalization of financial fraud analysis.
Wealth-related data play a critical role for governments and regulators, serving both as a foundation for designing and structuring laws and regulations and as an attribute that obligates citizens within those jurisdictions. Wealth analysis shapes stakeholder demand for, and citizen adherence to, wealth-reporting regulations, while informing governance requirements that define risk appetite and supervisory expectations. The dual use of wealth data—by regulators and the regulated alike—governs the development of wealth-related artificial intelligence functions.
Financial Intelligence integrates all dimensions of financial management, enabling oversight of risk decisions and the execution of financial functions, areas, and processes. The proposed Generative Financial Framework applies generative, semi-supervised, and hybrid Generative Financial Intelligence models across relevant financial functions and contexts. It further illustrates model oversight through considerations of data quality, feature engineering, model validation, and interpretability, alongside the embedding of decision-making approaches.
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