From Social Trust to Verifiable Claims: AI-Assisted Consumer Due Diligence in Financial Distribution — A Conceptual Framework and Reflexive Case Note

Financial products may be introduced through family, friendship, community events, or educational seminars before consumers know whether an interaction is personal, informational, or commercial. This independent conceptual preprint proposes CLEAR (Claims, Licensing, Entities, Assets, and Rewards/Redress), a source-auditable consumer due-diligence workflow assisted by generative AI. An anonymized first-person case note examines the boundaries among social trust, official registration, product scope, financial incentives, and custody of assets. Importantly, an initial suspicion that securities authorization was absent was revised after an official Canadian public register showed a relevant mutual-fund dealing-representative category. The paper distinguishes regulatory permission from product suitability, multilevel commission distribution from established fraud, and AI-generated suggestions from evidence. It offers an evidence ledger, ethical safeguards, stopping rules, testable propositions, and a prospective evaluation design. It does not establish misconduct or causal effectiveness, and it has not been peer reviewed.