FST Pay：面向青少年数字支付的确定性安全门控架构

中文摘要 AI 辅助

数字支付基础设施日益为青少年用户提供对实时金融服务的直接访问。虽然早期访问促进了金融素养和数字包容，但它也使年轻用户面临冲动消费、社会工程欺诈、未经授权的交易和商户剥削的严重风险。传统对策依赖于概率性机器学习或僵化的静态控制。然而，允许概率性或生成式人工智能（AI）模型直接影响实时支付授权会引入非确定性、不可预测的边界情况行为以及关键的审计漏洞。本文介绍了青少年金融安全支付（FST Pay）作为一种架构和形式化规范。FST Pay 基于一个不可变的操作边界：在实时授权路径上实施严格的确定性安全门控，并辅以解耦的下游 AI 解释。通过 UPI 等通道发起的交易需经过六项确定性不变量检查，涵盖支出限额、监护人共同签名政策、交易金额阈值、商户类别代码、时间访问间隔和硬件完整性约束。交易通过有序的、互斥的决策函数被严格分类为允许（ALLOW）、审查（REVIEW）或阻止（BLOCK）结果。高风险交易会触发异步的监护人共同签名工作流。生成式 AI 完全被置于结算流程的下游，仅消费已发布的决策后事件来生成自然语言的金融洞察，而不对账本持有任何修改权限。

英文摘要

Digital payment infrastructures increasingly provide adolescent users with direct access to real-time financial services. While early access promotes financial literacy and digital inclusion, it exposes young users to severe risks of impulsive spending, social engineering frauds, unauthorized transactions, and merchant exploitation. Conventional countermeasures rely on probabilistic machine learning or rigid static controls. However, allowing probabilistic or generative artificial intelligence (AI) models to directly influence real-time payment authorization introduces non-determinism, unpredictable edge-case behavior, and critical audit vulnerabilities. This paper introduces Financial Safety for Teens Pay (FST Pay) as an architectural and formal specification. FST Pay is founded on an immutable operational boundary: strict deterministic safety gating on the real-time authorization path coupled with decoupled downstream AI explanation. Transactions initiated via rails like UPI are subjected to six deterministic invariant checks covering spending limits, guardian co-sign policies, transaction amount thresholds, merchant category codes, temporal access intervals, and hardware integrity constraints. Transactions are classified strictly into ALLOW, REVIEW, or BLOCK outcomes through an ordered, mutually exclusive decision function. High-risk transactions trigger an asynchronous guardian co-sign workflow. Generative AI is relegated entirely downstream of settlement, consuming published post-decision events solely to generate natural-language financial insights without holding mutation privileges over the ledger.

发表机构

• Khaja Bandanawaz University (KBNU)（卡贾·班达纳瓦兹大学）

机构由 AI 辅助整理，请以论文原文为准。