Trustworthy Generative Intelligence for Fraud Foresight and Wealth Risk Governance
DOI:
https://doi.org/10.5281/9zpg8h87Keywords:
Generative Financial Intelligence, Trustworthy Artificial Intelligence, Financial Fraud Detection, Wealth Risk Governance, Regulatory Reasoning, Explainable Artificial Intelligence, Risk Analytics, Compliance Intelligence, Adaptive Governance, Financial Decision Intelligence, AI Governance, Trustworthy Machine Learning.Abstract
This analysis introduces the Generative Financial Intelligence Framework (GFIF), a conceptual and technological response to a specific failure mode in generative AI systems: the tendency of these models to produce outputs that are semantically plausible yet substantively disconnected from ground truth. This failure mode is illustrated through two analogies—flame-resistant clothing labeled as such without genuine fire resistance, and fake news articles that are grammatically coherent yet misleading or opinion-laden. In financial contexts, this same gap between surface plausibility and underlying accuracy manifests as a governance risk: models that appear trustworthy on the basis of fluent or confident output, but that fail to reliably detect fraud, support sound regulatory reasoning, or inform adaptive wealth-risk management.
GFIF addresses this gap by establishing a framework for evaluating and ensuring model trustworthiness in three interconnected domains: financial fraud prediction, regulatory compliance reasoning, and adaptive wealth-risk governance. The framework examines how regulatory-induced models—such as those responding to COVID-19-related policy shifts or energy-shortage regulations—can generate emergent risks when their outputs are treated as reliable without adequate verification against real-world outcomes. By situating this problem within existing fraud detection, compliance, and risk management literature, this analysis clarifies key terminology, establishes core theoretical principles, and proposes a conceptual model and set of operating assumptions for evaluating generative financial intelligence systems going forward.
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