FST Pay: Deterministic Safety-Gated Architecture for Youth Digital Payments

• URL: http://arxiv.org/abs/2609.11195v1
• Date: Thu, 10 Sep 2026 08:03:34 GMT
• Title: FST Pay: Deterministic Safety-Gated Architecture for Youth Digital Payments
• Abstract summary: This paper introduces Financial Safety for Teens Pay (FST Pay) as an architectural and formal specification.<n>FST Pay is founded on an immutable operational boundary: strict deterministic safety gating on the real-time authorization path.<n>Generative AI is relegated entirely downstream of settlement, consuming published post-decision events to generate natural-language financial insights.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Provably Secure Agent Guardrail [89.79561918065122]
  Existing defense architectures rely on empirical semantic guardrails and probabilistic large model adjudicators.<n>This paper proposes a new security paradigm for agents based on the fundamental limitations of logical reasoning.
  arXiv  Detail & Related papers  (2026-05-28T02:12:41Z)
• CyberAId: AI-Driven Cybersecurity for Financial Service Providers [0.7279162155456086]
  We present CyberAId, a model-agnostic, on-premise-deployable platform in which a Main Agent coordination layer, a Reporting capability, and specialist subagents operate within a shared runtime under bounded human-in-the-loop autonomy.
  arXiv  Detail & Related papers  (2026-05-03T14:15:11Z)
• GoAT-X: A Graph of Auditing Thoughts for Securing Token Transactions in Cross-Chain Contracts [52.51342355102833]
  Cross-chain bridges, the critical infrastructure of the multi-chain ecosystem, have become a primary target for attackers.<n>Existing defenses, such as bytecode-level static analysis, are ill-equipped to handle the semantic complexity of cross-chain interactions.<n>We propose GoAT-X, a framework that shifts automated cross-chain smart contracts auditing from pattern matching toward systematic first-principles verification.
  arXiv  Detail & Related papers  (2026-04-27T11:34:21Z)
• CORA: Conformal Risk-Controlled Agents for Safeguarded Mobile GUI Automation [68.53387633351484]
  We propose CORA (COnformal Risk-controlled GUI Agent), a post-policy, pre-action safeguarding framework that provides statistical guarantees on harmful executed actions.<n> CORA reformulates safety as selective action execution: we train a Guardian model to estimate action-conditional risk for each proposed step.<n>To rigorously evaluate this paradigm, we introduce Phone-Harm, a new benchmark of mobile safety violations with step-level harm labels.
  arXiv  Detail & Related papers  (2026-04-10T09:41:21Z)
• Quantifying Trust: Financial Risk Management for Trustworthy AI Agents [27.23487816533652]
  We introduce ARS, a payment settlement standard for AI-mediated transactions.<n>ARS integrates risk assessment, underwriting, and compensation into a single transaction framework.<n>ARS shifts trust from an implicit expectation about model behavior to an explicit, measurable, and enforceable product guarantee.
  arXiv  Detail & Related papers  (2026-04-05T05:42:20Z)
• An Adaptive Neuro-Fuzzy Blockchain-AI Framework for Secure and Intelligent FinTech Transactions [0.0]
  This paper presented an Adaptive Neuro-Fuzzy-AI Framework (ANFB-AI) to achieve security in FinTech transactions.<n>An explicit mathematical model is created to explain the transaction integrity, adaptive threat classification, and unified risk based decision-making.<n>Massive simulations are performed in normal, moderate, and high-fraud conditions.
  arXiv  Detail & Related papers  (2026-03-25T01:28:02Z)
• A Dual-Path Generative Framework for Zero-Day Fraud Detection in Banking Systems [0.2864713389096699]
  High-frequency banking environments face a critical trade-off between low-latency fraud detection and regulatory explainability.<n>Traditional rule-based and discriminative models struggle with "zero-day" attacks due to extreme class imbalance and the lack of historical precedents.<n>This paper proposes a Dual-Path Generative Framework that decouples real-time anomaly detection from offline adversarial training.
  arXiv  Detail & Related papers  (2026-02-17T10:20:40Z)
• Counterfactual Invariant Envelopes for Financial UX: Safety-Lattice Feature-Flag Governance in Crypto-Enabled Streaming [0.0]
  In crypto-enabled live streaming, naive rollouts can create non-obvious risk.<n>This paper introduces a novel invention candidate, a Counterfactual Invariant Envelope governor.<n>We formalize rollout risk, define invariant constraints across feature combinations, and propose a controller that adapts exposure using leading abuse signals.
  arXiv  Detail & Related papers  (2026-01-23T18:36:22Z)
• Foundation Models for Logistics: Toward Certifiable, Conversational Planning Interfaces [59.80143393787701]
  Large language models (LLMs) can handle uncertainty and promise to accelerate replanning while lowering the barrier to entry.<n>We introduce a neurosymbolic framework that pairs the accessibility of natural-language dialogue with verifiable guarantees on goal interpretation.<n>A lightweight model, fine-tuned on just 100 uncertainty-filtered examples, surpasses the zero-shot performance of GPT-4.1 while cutting inference latency by nearly 50%.
  arXiv  Detail & Related papers  (2025-07-15T14:24:01Z)
• Are You Getting What You Pay For? Auditing Model Substitution in LLM APIs [71.7892165868749]
  Commercial Large Language Model (LLM) APIs create a fundamental trust problem.<n>Users pay for specific models but have no guarantee that providers deliver them faithfully.<n>We formalize this model substitution problem and evaluate detection methods under realistic adversarial conditions.<n>We propose and evaluate the use of Trusted Execution Environments (TEEs) as one practical and robust solution.
  arXiv  Detail & Related papers  (2025-04-07T03:57:41Z)
• Deep Learning Approaches for Anti-Money Laundering on Mobile Transactions: Review, Framework, and Directions [51.43521977132062]
  Money laundering is a financial crime that obscures the origin of illicit funds.<n>The proliferation of mobile payment platforms and smart IoT devices has significantly complicated anti-money laundering investigations.<n>This paper conducts a comprehensive review of deep learning solutions and the challenges associated with their use in AML.
  arXiv  Detail & Related papers  (2025-03-13T05:19:44Z)
• Regulation conform DLT-operable payment adapter based on trustless - justified trust combined generalized state channels [77.34726150561087]
  Economy of Things (EoT) will be based on software agents running on peer-to-peer trustless networks. We give an overview of current solutions that differ in their fundamental values and technological possibilities. We propose to combine the strengths of the crypto based, decentralized trustless elements with established and well regulated means of payment.
  arXiv  Detail & Related papers  (2020-07-03T10:45:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.