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FST Pay: Deterministic Safety-Gated Architecture for Youth Digital Payments Shaikh Mohammed Burhan, Syed Farhaan Quadri, and Dr. Tabassum Nahid Sultana Department of Computer Science and Engineering (CSE) Khaja Bandanawaz University (KBNU), Kalaburagi, Karnataka 585104, India Email: [email protected], [email protected], [email protected]
arXiv:2609.11195v1 [cs.SE] 10 Sep 2026
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 edgecase behavior, and critical audit vulnerabilities. This paper introduces Financial Safety for Teens Pay (FST Pay) as an architectural and formal specification; empirical validation is scoped for future testbed implementations. 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 interoperable rails such as the Unified Payments Interface (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/device integrity constraints. Transactions are classified strictly into ALLOW, REVIEW, or BLOCK outcomes without probabilistic ambiguity through an ordered, mutually exclusive decision function. High-risk transactions trigger an asynchronous guardian cosign workflow. Generative AI is relegated entirely downstream of settlement, consuming published post-decision events solely to generate natural-language financial literacy insights and risk rationales without holding mutation privileges over the ledger. We formally specify the safety invariants, present end-to-end TikZ architectural models, and outline an auditable relational data schema. Index Terms—Digital payments, youth financial safety, deterministic authorization, Unified Payments Interface, guardian approval, explainable AI, transaction safety.
I. I NTRODUCTION EAL-TIME account-to-account payment systems have reshaped retail commerce worldwide [1], [2]. In particular, open digital infrastructures such as India’s Unified Payments Interface (UPI), developed by the National Payments Corporation of India (NPCI) and regulated by the Reserve
R
Bank of India, process billions of high-velocity, low-cost financial transactions monthly [3], [4]. The rapid democratization of mobile payment devices has lowered the entry age for digital transactions, granting adolescent demographics direct access to electronic fiat currency [1]. Extending uninhibited transactional autonomy to young, financially inexperienced users exposes them to asymmetric risks. Adolescents routinely exhibit vulnerability to impulsive overspending, deceptive gamification patterns (such as loot boxes and in-app microtransactions), phishing scams, and coercive social engineering [3], [5]. Conventional retail banking architectures typically approach account safety by treating users as fully autonomous legal adults, enforcing standard multi-factor authentication (MFA) but lacking fine-grained parental oversight mechanisms [6], [7]. In contrast, custodial bank accounts often impose total lockouts that impede autonomous learning for minor dependents [8], [9]. Modern payment networks employ advanced machine learning (ML) classifiers to detect fraudulent activities [10], [11], [12]. While supervised classifiers, graph neural networks, and anomaly detectors excel at macroscopic fraud identification, their probabilistic nature poses fundamental hazards when such classifiers are directly embedded as primary authorization arbiters for minors. An authorization failure driven by latent weight drift or opaque statistical boundaries lacks the auditability and predictable guarantees essential in legal guardianship [13]. The National Institute of Standards and Technology Generative AI Profile (NIST AI 600-1) [14] notes that foundation models suffer from stochastic hallucinations, prompt injection vulnerabilities, and non-deterministic decision paths [15]. Allowing a generative model or an uncalibrated probabilistic classifier to directly trigger, modify, or decline a financial payment violates foundational financial safety tenets and model risk governance principles [13], [20]. Explainable artificial intelligence (XAI) has emerged as an indispensable requirement in financial technologies [16]. When an automated system denies an adolescent’s payment or suspends an account, merely presenting an obscure error code yields zero educational value and induces frustration [16]. Parents and adolescent users require clear, natural-language rationales detailing why a transaction was restricted and how to establish safer spending patterns [17], [18]. To resolve the tension between mathematical determinism in financial clearance and conversational adaptability in user explanation, this paper presents Financial Safety for Teens Pay (FST Pay) as an architectural and formal specification.
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The core philosophical premise dictates that zero probabilistic or generative components may exist on the synchronous authorization rail. Instead, every transaction request is passed through a deterministic verification engine that evaluates explicit, parentally configured safety invariants across multidimensional parameters. Only after an immutable state has been permanently committed to the ledger does an isolated downstream generative AI engine consume the audit event to compose plain-language educational summaries. The main contributions of this paper are summarized as follows: 1) We design and formalize a deterministic safety-gating architecture that eliminates probabilistic latency and hallucination risks from the real-time financial authorization path. 2) We establish a dual-state guardian co-sign mechanism that cleanly intercepts high-risk or threshold-exceeding transactions and routes them into a synchronous multiparty consent hold. 3) We formulate a strict non-interference invariant, enforced architecturally through capability isolation, preventing post-decision generative AI models from mutating ledger states. 4) We detail a complete systems architecture, an end-to-end Unified Modeling Language (UML) interaction pipeline, a multi-participant sequence flow, and a fully normalized relational schema optimized for auditable youth payment systems. II. R ELATED W ORK A. Youth Financial Inclusion and Payment Rails The digital transformation of retail banking has catalyzed research into the financial socialization of youth. The Organisation for Economic Co-operation and Development (OECD) emphasizes that youth-focused financial inclusion must balance access with protective scaffolding, as minors routinely face unique behavioral and digital exploitation threats [1]. Interoperable architectures such as UPI offer instant settlement across bank accounts [19]. While UPI incorporates end-toend cryptographic signatures and device-binding protocols, it delegates user-level risk limits and behavioral spending rules to participating payment service provider (PSP) applications. Most commercial banking applications enforce coarse account-level caps rather than context-aware parental control policies [19]. Commercial youth fintech offerings (such as Greenlight and FamPay) have popularized prepaid family allowances, but remain bound to proprietary static ledger limits without verifiable formal invariant guarantees or decoupled explanatory artificial intelligence. B. Machine Learning in Financial Fraud Detection Financial transaction environments present severe class imbalance, extreme throughput requirements, and evolving adversarial tactics [11], [12]. Contemporary research categorizes machine learning models for payment fraud, noting that while deep learning and neural network architectures
achieve high Area Under the Receiver Operating Characteristic Curve (AUROC), they require significant computational overhead and exhibit vulnerability to concept drift [10], [4]. Furthermore, probabilistic systems struggle to provide the hard mathematical guarantees required for statutory compliance and contractual policy enforcement. C. Explainable and Generative AI in Financial Systems Explainability in financial algorithms is vital for both regulatory compliance and user trust [16]. Prior literature on explainable artificial intelligence (XAI) across financial information systems highlights methods like SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) used to unpack post-hoc credit and fraud models. Černevičienė and Kabašinskas [16] synthesized XAI taxonomies, arguing that transparent, interpretable outputs are foundational for user-facing automated decision systems. With the emergence of Large Language Models (LLMs), recent research has explored conversational agents for customer interaction and contextual spending analytics [35], [36]. However, as cataloged in the NIST Generative AI Risk Management Profile (NIST AI 600-1) [14], foundation models exhibit non-negligible failure modes including factual hallucinations, prompt injection vulnerabilities, and non-deterministic output paths. D. Research Gap and Architectural Opportunity Existing financial platforms typically commit to one of two suboptimal paradigms: either they deploy legacy static rules that lack contextual explanation, or they experiment with end-to-end neural pipelines that compromise auditability and latency guarantees [21]. No unified framework explicitly addresses youth digital safety by synchronizing deterministic policy validation, real-time guardian co-signing, and asynchronous, read-only generative explanation. FST Pay is architected specifically to address this gap. III. M ETHODOLOGY A. Foundational Baseline and Model Evolution The mathematical formalization underpinning FST Pay builds upon classical Attribute-Based Access Control (ABAC) theory and deterministic finite-state transaction machines [13], [22]. In standard access control paradigms, an authorization request is evaluated against a static Boolean predicate over subject, object, and environment attributes. However, digital youth payments introduce dynamic financial dependencies—such as rolling diurnal expenditures, fixed categorical Merchant Category Codes (MCC), diurnal curfew intervals, and realtime multi-party supervisory escalation—that standard access models cannot express. To bridge this gap, we adapt and extend the generalized authorization tuple model into a six-dimensional invariant evaluation vector ℐ. Rather than permitting probabilistic scoring or unconstrained language model evaluation on the authorization path, our model maps each transaction request deterministically onto a closed ternary outcome space
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Ω = {ALLOW, REVIEW, BLOCK}. Below, we define the constituent equations and explain how they advance traditional payment control logic. B. Mathematical Formalization of the Safety Invariant Model Let 𝒯req denote the set of all valid transaction initiation requests. A specific payment initiation request 𝑅 ∈ 𝒯req from an adolescent user is formalized as an immutable tuple: 𝑅 = (𝑢, 𝑚, 𝑎, 𝑡, 𝑑, 𝑐)
(1)
where 𝑢 ∈ 𝒰 represents the authenticated adolescent user identity (e.g., User_2317), 𝑚 ∈ ℳ denotes the counterparty payee identifier, 𝑎 ∈ R+ denotes the transactional quantum in Indian Rupees (Rs. INR), 𝑡 ∈ 𝒯 represents the timestamp, 𝑑 ∈ 𝒟dev denotes hardware device telemetry, and 𝑐 ∈ 𝒞 encapsulates contextual environment attributes. Prior to invariant evaluation, we define the merchant universe partition. Let the global counterparty domain ℳ be partitioned into three pairwise disjoint sets: ℳ = ℳauto ∪ ℳcosign ∪ ℳprohibited
(2)
where ℳauto = ℳp2p_whitelist ∪ ℳpreapproved_retail denotes preauthorized peer contacts and verified minor-safe merchants, ℳcosign denotes unverified or high-value categories requiring supervisory approval, and ℳprohibited represents blacklisted merchant categories (e.g., gambling or adult services). Furthermore, let 𝜃cosign ∈ R+ represent the guardianconfigured single-transaction monetary threshold above which autonomous execution is barred. FST Pay evaluates request 𝑅 against six deterministic safety invariants ℐ = {𝐼𝑠 , 𝐼auto , 𝐼𝑎 , 𝐼𝑚 , 𝐼𝑡 , 𝐼𝑟 }: 1) Rolling Velocity Constraint (𝐼𝑠 ): Uncontrolled fund drainage is prevented by tracking historical expenditure ℋ𝑢 (Δ𝑡) over a moving interval Δ𝑡, validating that the aggregate quantum respects the velocity cap 𝐿Δ𝑡 : {︃ ∑︀ 1, if 𝑎 + 𝑘∈ℋ𝑢 (Δ𝑡) 𝑎𝑘 ≤ 𝐿Δ𝑡 𝐼𝑠 (𝑅) = (3) 0, otherwise When breached (𝐼𝑠 = 0), the pipeline does not discard the request; it diverts the payment into supervisory review for explicit guardian clearance. 2) Autonomous Clearance Invariant (𝐼auto ): Low-risk transactions within established personal allowances and whitelisted networks should proceed without guardian latency: {︃ 1, if 𝑚 ∈ ℳauto ∧ 𝑎 < 𝜃cosign 𝐼auto (𝑅) = (4) 0, otherwise (co-sign required) A value of 0 indicates that the transaction involves an unfamiliar merchant, a non-whitelisted peer, or a monetary sum exceeding 𝜃cosign , necessitating real-time parental consent. 3) Single Transaction Quantum Ceiling (𝐼𝑎 ): To defend the account against sudden balance exhaustion, a hard pertransaction ceiling 𝐴max is enforced: {︃ 1, if 𝑎 ≤ 𝐴max 𝐼𝑎 (𝑅) = (5) 0, otherwise In contrast to velocity limits, an overage here represents a parameter violation that halts the checkout pipeline immediately.
4) Payee Category Restriction (𝐼𝑚 ): Target merchant classification codes are cross-referenced with the prohibited partition ℳprohibited [23]: {︃ 1, if MCC(𝑚) ∈ / ℳprohibited (6) 𝐼𝑚 (𝑅) = 0, otherwise Interactions with restricted business sectors (e.g., MCC 7995) result in a non-negotiable block. 5) Curfew Interval Enforcement (𝐼𝑡 ): Transactions are confined to diurnal operating windows [𝑇open , 𝑇close ], restricting transaction execution during unauthorized nighttime hours: {︃ 1, if time(𝑡) ∈ [𝑇open , 𝑇close ] (7) 𝐼𝑡 (𝑅) = 0, otherwise 6) Platform Root-of-Trust Attestation (𝐼𝑟 ): Cryptographic attestation and hardware keystore validity are verified via the platform integrity predicate Φhw (𝑑): {︃ 1, if Φhw (𝑑) = TRUE 𝐼𝑟 (𝑅) = (8) 0, otherwise C. Deterministic Decision Function The composite decision engine maps each transaction request deterministically to a discrete outcome Ω = {ALLOW, REVIEW, BLOCK}. The function signature is formalized strictly as 𝒟 : 𝒯req → Ω, with invariant vector ℐ(𝑅) evaluated internally over request 𝑅: ⎧ BLOCK, if 𝐼𝑎 (𝑅) = 0 ∨ 𝐼𝑚 (𝑅) = 0 ⎪ ⎪ ⎪ ⎨ ∨𝐼𝑡 (𝑅) = 0 ∨ 𝐼𝑟 (𝑅) = 0 (9) 𝒟(𝑅) = ⎪ REVIEW, if 𝐼 auto (𝑅) = 0 ∨ 𝐼𝑠 (𝑅) = 0 ⎪ ⎪ ⋀︀ ⎩ ALLOW, if 𝑗∈{𝑠,auto,𝑎,𝑚,𝑡,𝑟} 𝐼𝑗 (𝑅) = 1 Operational precedence is evaluated top-down: fatal policy violations (𝐼𝑎 , 𝐼𝑚 , 𝐼𝑡 , 𝐼𝑟 ) immediately yield BLOCK. In their absence, supervisory escalation conditions (𝐼auto , 𝐼𝑠 ) route the request to REVIEW. The transaction resolves to ALLOW if and only if all six invariants evaluate to 1. D. The Architectural Non-Interference Invariant Downstream AI explanation is defined as a decoupled function 𝑓AI parameterized solely by post-decision state: 𝐸 = 𝑓AI (𝑋, 𝒟)
(10)
where 𝑋 = (𝑅, ℐ(𝑅), 𝜏 ) represents the immutable execution context committed at ledger time 𝜏 . To structurally isolate the financial ledger against prompt injection vulnerabilities [24], FST Pay specifies the capability boundary: Cap(𝑓AI ) ∩ {write-ledger, write-wallet, mutate-auth} = ∅ (11) Equation (11) specifies that explanation services hold no mutation rights. This isolation is enforced via read-only Kafka consumer credentials and separate database connection pools, ensuring that prompt outputs cannot alter authorization states 𝒟.
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IV. S YSTEM A RCHITECTURE A. Two-Stage Structural Pipeline FST Pay enforces an architectural segregation between authorization authority and analytical explanation across two stages: ∙ Stage 1: Deterministic Safety-Gating (PreAuthorization): A low-latency evaluation service implemented as a native microservice. Incoming payment requests are evaluated concurrently across invariant vector ℐ. In-memory rule lookups execute in sub-millisecond time (< 1 ms), supporting an overall pre-authorization gateway budget target of 𝑝99 < 15 ms without probabilistic dependencies on the critical rail. ∙ Stage 2: Downstream AI Explanation (Post-Decision): An out-of-band analytical service bound strictly to committed event streams. It consumes verified ledger records, parses contextual metadata, and invokes isolated LLM endpoints to synthesize pedagogical summaries and parental compliance reports. Figure 1 illustrates this multi-tier architecture from client presentation down to banking infrastructure. B. Component Decomposition In our reference software implementation, core transactional services are structured in Java using Spring Boot, with downstream workers deployed in Python: 1) Adolescent Client Application: React Native mobile client maintaining encrypted local credential caches, biometric device authentication hooks, and budget visualization displays. 2) Guardian Supervisory Application: Native companion interface dispatching push-notification webhooks for real-time transaction co-signing and limit adjustments. 3) API Gateway & Ingress Service: Enforces Transport Layer Security (TLS) 1.3 termination, rate-limiting, and short-lived JSON Web Token (JWT) signature verification. 4) Deterministic Rule Evaluator: Stateless Spring Boot service maintaining in-memory Redis caches of active guardian policies and sliding-window velocity counters. 5) Core Payment Orchestrator: Interfaces securely via ISO 20022 messaging structures (pacs.008) with underlying UPI Switch rails and National Financial Switch (NFS) endpoints [27]. 6) Downstream Generative Service: Decoupled asynchronous worker service executing audited prompts against an isolated LLM endpoint to generate structured explainability logs. V. PAYMENT D ECISION AND AUTHORIZATION P IPELINE The authorization pipeline executes as a strictly ordered procedural gate prior to financial dispatch, guaranteeing predictable settlement without statistical ambiguity: 1) Syntactic Ingress & Identity Validation: The API gateway validates syntactic conformity, cryptographic
payload signatures, replay protection nonces, and timestamp drift constraints (|𝑡 − 𝑡gateway | ≤ 5000 ms). If the account state is inactive or suspended, execution halts immediately with a deterministic BLOCK response. 2) Velocity & Cumulative Expenditure Assertion (𝐼𝑠 , 𝐼𝑎 ): The engine evaluates cached ledger state to assert that transactional quantum 𝑎 respects both the single transaction cap 𝐴max and the rolling window ceiling 𝐿Δ𝑡 . Fatal overages transition to BLOCK, whereas velocity-limit breaches trigger guardian escalation. 3) Category & Curfew Enforcement (𝐼𝑚 , 𝐼𝑡 ): Payee classifications are cross-checked against blacklisted Merchant Category Codes (e.g., MCC 7995 for gambling) [23], and current time is matched against curfew intervals [𝑇open , 𝑇close ]. Violations yield an immediate, non-overridable BLOCK. 4) Guardian Co-Sign Escalation Protocol (𝐼auto ): When the request exceeds the minor’s autonomous limit 𝜃cosign or targets an unverified counterparty (𝐼auto = 0), the state machine transitions to REVIEW. A cryptographic push challenge is transmitted to the guardian’s registered device. The transaction remains suspended in a pending authorization state until an authenticated biometric cosignature is received or an expiry threshold elapses. 5) Contextual Hardware Verification (𝐼𝑟 ): Device telemetry is evaluated against hardware-backed keystore integrity signals. If anomalous device manipulation is flagged, the pipeline halts with BLOCK. 6) Atomic Settlement Dispatch: Upon satisfying all gating invariants (or receiving verified guardian consent), the orchestrator initiates settlement over interoperable UPI core switches and atomically updates the ledger. VI. M ULTI -PARTICIPANT P ROCESS S EQUENCE The end-to-end multi-participant interaction sequence is modeled in Figure 2. The primary actors and systemic entities include: 1) Adolescent User Application: Authenticates the local user session, captures merchant destination parameters, and submits the cryptographically signed payment intent. 2) API Gateway: Terminates ingress traffic, verifies mutual TLS (mTLS), and dispatches the sanitized intent to the deterministic engine. 3) Safety Gating Engine: Concurrently evaluates invariant vector ℐ against active in-memory state caches. 4) Guardian Application: Receives synchronous push cosign challenges for transactions requiring supervisory consent. 5) UPI Rail / Core Switch: Executes fund transfers across participating payment service providers and commercial banks. 6) Aurora Ledger Core: Records immutable double-entry journal entries under Atomicity, Consistency, Isolation, Durability (ACID) transactional isolation. 7) Downstream AI Worker: Asynchronously consumes transaction finality events from Kafka queues and renders natural-language explanations.
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TIER 1: PRESENTATION & ADAPTIVE CLIENT LAYER
iOS Native App (Swift / Keystore)
Android Native (Kotlin / Biometrics)
React Web Portal (Merchant / Admin)
API Gateway & Security Ingress (Envoy / Cloudflare WAF) Transport Layer Security (TLS) 1.3 Termination ∙ OAuth2/OIDC Token Validation ∙ Rate Limiting & Anti-DDoS TIER 3: CORE BACKEND MICROSERVICES LAYER (SPRING BOOT & PYTHON WORKERS)
Double-Entry Ledger Service
User & KYC Service
Deterministic Gating Rules Engine
Payment Rail Orchestrator
Downstream AI Explanation Worker
TIER 4: DATA PERSISTENCE & EVENT STREAMING INFRASTRUCTURE
PostgreSQL Aurora (ACID Financial Ledger)
Redis Cluster (Velocity Limit Caching)
Apache Kafka Bus (Audit Event Streaming) [25]
TIER 5: EXTERNAL BANKING, UPI 2.0 SWITCH & ISSUING RAILS [26] NPCI Unified Payments Interface ∙ Card Networks (RuPay / Visa) ∙ Mobile Push Notification Webhooks
Fig. 1. FST Pay: Complete multi-tier production software system architecture diagram.
As demonstrated in Figure 2, rail settlement and ledger commitment occur strictly before event publication across the authorization boundary. VII. DATA M ODEL To support deterministic rule verification, low-latency audit logging, and isolated explanation ingestion, FST Pay employs the relational database schema illustrated in Figure 3: 1) USER: Stores master identities, distinguishing adolescent accounts (ROLE_TEEN) from supervisory adult entities (ROLE_GUARDIAN). 2) GUARDIAN_RELATIONSHIP: Represents legal guardian-dependent pairs, linking supervisory accounts to dependent wallets. 3) WALLET & SPENDING_LIMIT: Decouples stored balances from dynamic limits, maintaining JSONBencoded merchant restrictions and rolling ceilings. 4) TRANSACTION: The immutable double-entry ledger table enforced with unique idempotency keys to prevent duplicate clearing under transient network retries [22]. 5) APPROVAL_REQUEST: Records asynchronous cosign states, resolution timestamps, and guardian cryptographic signatures. 6) AI_EXPLANATION_LOG & TRANSACTION_EXPLANATION: Functionally isolated audit tables tracking prompt token usage, model identifiers, inferencing latencies, and generated natural-language explanations. VIII. I MPLEMENTATION C ONSIDERATIONS A. Backend Stack and Microservice Orchestration The FST Pay reference architecture is organized into independent microservices using Java and Spring Boot for deterministic invariant evaluation, alongside Python services for asynchronous LLM orchestration: ∙ Deterministic Invariant Service: Configured with embedded execution rules to achieve sub-millisecond inmemory evaluation. It maintains active user spending
sums in a high-availability Redis cluster using slidingwindow counter structures. ∙ Payment Settlement Adapter: Integrates with the UPI Switch via encrypted mutual TLS. It formats transaction requests following NPCI specifications and handles settlement responses. ∙ Asynchronous Event Dispatcher: Leverages an Apache Kafka event backbone. Once a transaction is settled or rejected, an event is emitted to a partitioned topic. ∙ Downstream Generative Worker: A decoupled Python service utilizing asynchronous workers to consume Kafka events, format prompts, and query language model endpoints.
B. Idempotency and Concurrency Control Financial correctness requires strict concurrency control to prevent double-spending. When an adolescent client submits a payment request, the client generates a unique UUIDv4 idempotency key. The payment gateway verifies this key against the database using atomic insertion [22]: INSERT INTO transaction (idempotency_key, ...) VALUES (?, ...) ON CONFLICT DO NOTHING; If a concurrent duplicate request arrives during authorization, it is intercepted and rejected immediately, ensuring that account balances are updated atomically without race conditions [22].
IX. P ROPOSED E VALUATION F RAMEWORK AND T HEORETICAL A NALYSIS A. Evaluation Methodology In strict alignment with empirical reporting standards, no simulated or fabricated benchmark metrics are reported as proven experimental results. Instead, we formalize a rigorous proposed evaluation framework to validate FST Pay in future testbed implementations, as detailed in Table I.
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Teen App User_2317
API Gateway Edge Routing
Guardian App Supervisor
Safety Gating Invariant Core
UPI Rail NPCI Switch
Aurora Ledger PostgreSQL DB
Downstream AI Explanation Agent
1. Initiate Payment Request (Rs. 1,200) 2. Validate Session & Nonce
3. Execute Invariant Rules
4. Co-Sign Approval Webhook 5. Guardian Co-Sign Response (Approved) 6. Authorized Payment Intent 7. UPI Settlement Request 8. Settlement Acknowledged (UTR) 9. Commit Settlement Record MANDATORY AUTHORIZATION BOUNDARY — GENERATIVE AI CONSUMES AUDITED EVENTS ONLY
10. Publish Transaction Event 11. Dispatch AI Summary & Educational Insight
Fig. 2. Multi-participant sequence diagram illustrating deterministic payment clearance and downstream asynchronous explanation dispatch. USER PK user_id: UUID name: VARCHAR(100) email: VARCHAR(120) mobile_number: VARCHAR(20) role: VARCHAR(20) account_status: VARCHAR(20) created_at: TIMESTAMP
1:1
WALLET PK wallet_id: UUID FK user_id: UUID currency: VARCHAR(3) current_balance: NUMERIC(12,2) locked_balance: NUMERIC(12,2) created_at: TIMESTAMP
1:N
TRANSACTION PK tx_id: UUID FK wallet_id: UUID merchant_id: VARCHAR(64) amount_inr: NUMERIC(10,2) status: VARCHAR(20) idempotency_key: UUID created_at: TIMESTAMP
1:0..1
APPROVAL_REQUEST PK approval_id: UUID FK tx_id: UUID FK guardian_id: UUID status: VARCHAR(20) resolved_at: TIMESTAMP created_at: TIMESTAMP
1:1 1:N
GUARDIAN_RELATIONSHIP PK relationship_id: UUID FK guardian_id: UUID FK dependent_id: UUID is_active: BOOLEAN created_at: TIMESTAMP
1:1
1:1
SPENDING_LIMIT PK limit_id: UUID FK relationship_id: UUID daily_limit_inr: NUMERIC(10,2) per_transaction_inr: NUMERIC(10,2) category_limit: JSONB is_active: BOOLEAN
AI_EXPLANATION_LOG PK log_id: UUID FK tx_id: UUID prompt_tokens: INTEGER completion_tokens: INTEGER latency_ms: INTEGER model_name: VARCHAR(50) created_at: TIMESTAMP
TRANSACTION_EXPLANATION PK explanation_id: UUID FK tx_id: UUID explanation_text: TEXT model_version: VARCHAR(30) language: VARCHAR(10) created_at: TIMESTAMP
Fig. 3. Normalized relational database schema capturing core transactional invariants, guardian relationships, and decoupled AI explanation logs.
TABLE I P ROPOSED E VALUATION F RAMEWORK AND O PERATIONAL M ETRICS Evaluation Dimension
Target Metric
Verification Method
Deterministic Latency
Pipeline duration (𝑝99 < 15 ms)
Ingress-to-egress Application Performance Monitoring (APM) distributed tracing
Policy Correctness
Zero false allowances on restricted MCCs
Automated suite with combinatorial inputs
Guardian Latency
Push-to-response duration (𝑡cosign )
Asynchronous roundtrip event tracking
Fault Isolation
System uptime under AI endpoint failure
Chaos engineering fault injection
Audit Completeness
Ledger traceability (100% target)
Cryptographic log verification
ledger
B. Theoretical Comparative Analysis
Table II presents a structured theoretical comparison contrasting FST Pay against traditional payment paradigms: Purely Probabilistic Fraud Detection, Unconstrained Generative AI Agents, and Conventional Static Retail Banking.
X. S ECURITY AND P RIVACY C ONSIDERATIONS A. Zero-Trust Ingress and Cryptographic Device Binding All API interactions require mutual TLS (mTLS) with public key pinning [28]. The adolescent mobile client establishes session authority using hardware-backed cryptographic keystores (e.g., Android Keystore, iOS Secure Enclave) [29]. Transactions require biometric validation (fingerprint or facial recognition) combined with short-lived asymmetric authorization tokens [30]. B. Isolation of the Generative Layer In compliance with NIST AI 600-1 recommendations for generative AI safety [14], the downstream explanation engine operates under least-privilege constraints [31]. The service possesses read-only API access to the event queue. It has no network routes, database permissions, or API credentials capable of executing payment settlements, updating wallet balances, or altering authorization outcomes. C. Data Minimization and Privacy Preservation Adolescent user privacy is protected through strict data sanitization before event ingestion [32]: ∙ Prompts dispatched to the downstream LLM contain zero personally identifiable information (PII). All user IDs, mobile numbers, and bank account numbers are tokenized or stripped [33].
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TABLE II T HEORETICAL C OMPARISON OF A RCHITECTURAL PARADIGMS (Q UALITATIVE D ESIGN -T IME P ROPERTIES ) Architectural Attribute
Purely Probabilistic ML
Generative AI Agent
Static Retail Banking
FST Pay (Proposed)
Decision Determinism
Low drift) [34]
Nondeterministic (stochastic)
High (rigid Boolean rules)
High (strict invariants)
Authorization Latency
Moderate (pipeline delay)
High (exceeds SLA)
Minimal (static lookup)
Design Target: 𝑝99 15 ms
Explainability Mechanism
Post-hoc LIME) [35]
Direct generative output
Error codes only
Asynchronous Decoupled AI
Hallucination Hazard
Not applicable
Critical risk [36]
Zero
Zero on Auth Rail (Isolated)
Guardian Oversight
Absent or manual
Unbounded
Binary account locks
Granular Co-Sign Hold
Audit Integrity
Complex model weights
Non-reproducible
Standard database log
Immutable Log
(probabilistic
(SHAP
/
Only broad contextual descriptors (e.g., transaction amount, merchant category, policy rule triggered) are provided to the explanation model. ∙ System logs are retained in encrypted cold storage with time-to-live (TTL) expiration schedules aligned with youth data privacy frameworks.
∙
XI. L IMITATIONS AND F UTURE W ORK While FST Pay establishes a robust architectural framework, several operational limitations warrant discussion: 1) Absence of Production Benchmark Data: As emphasized throughout this paper, FST Pay has not yet undergone wide-scale consumer field deployment. Empirical validation across diverse user cohorts remains future work. 2) Guardian Response Latency: The REVIEW state depends on timely human parental intervention. If a guardian’s device is offline or notifications are missed, transaction fulfillment will experience delays. Future work will investigate automated fallback policies and temporary micro-allowance overrides. 3) Evolving Merchant Categories: Payee classification depends on accurate Merchant Category Codes (MCC). Malicious or deceptive merchants misrepresenting their category could bypass category-based invariants [37]. Developing adaptive, verified merchant registries is an ongoing objective. 4) Lack of Machine-Checked Formal Verification: The non-interference property is currently enforced via architectural capability partitioning. Machine-checked verification using static capability analyzers or automated model checkers is scoped for future work. XII. C ONCLUSION This paper presented Financial Safety for Teens Pay (FST Pay), a digital payment safety architecture designed specifically for youth-oriented financial ecosystems. By establishing a clear architectural boundary between real-time deterministic safety gating and downstream generative explanation, FST Pay is designed to resolve the critical tradeoff between transaction safety and user-centric transparency.
checkout
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Multi-Stage
The deterministic authorization engine evaluates payment requests across six comprehensive safety invariants, enforcing parentally configured spending limits, merchant restrictions, and guardian co-sign holds with sub-millisecond rule evaluation predictability. Meanwhile, the decoupled downstream generative AI engine receives finalized, read-only transaction events to generate plain-language explanations and financial literacy guidance without possessing authority over the payment ledger. This separation is architected to eliminate AI hallucination risks from financial clearance and to provide a structured foundation for future implementation and empirical evaluation in real-world digital payment platforms. R EFERENCES [1] Organisation for Economic Co-operation and Development, “Advancing the Digital Financial Inclusion of Youth,” OECD Publishing, Paris, Tech. Rep., 2020. [View] [2] S. Basavesh, S. VH, and P. Tandan, “The Impact of Unified Payment Interface (UPI) on Small Business: A Study with Reference to Bangalore City,” International Scientific Journal of Engineering and Management, vol. 3, no. 2, pp. 1–10, 2024. [View] [3] A. Kukreja, “Evidence from a Quantitative Study on Digital Payments, E-Commerce and the Changing Spending Pattern of Teenagers,” International Journal of Social Sciences and Economic Research, vol. 9, no. 2, pp. 789–802, 2024. [View] [4] M. Habibpour et al., “Uncertainty-Aware Credit Card Fraud Detection Using Deep Learning,” Engineering Applications of Artificial Intelligence, vol. 123, p. 106249, 2023. [View] [5] O. V. Maslennikov and N. V. Maslennikova, “New Risks to the Young People as a Result of Digital Finance Development in the Russian Federation,” Finance and Credit, vol. 27, no. 4, pp. 890–907, 2021. [View] [6] K. Thomas, A. Joinson, and D. S. Fraser, “Investigating the Salience of Privacy and Security in Online Family Banking,” in Proc. ACM Int. Conf. Human Factors in Computing Systems (CHI), 2024, pp. 1–14. [View] [7] Y. Choong, M. Theofanos, K. Renaud, and S. Prior, “Exploring Children’s Authentication Knowledge and Practices,” in Proc. Workshop on Usable Security (USEC), Internet Society, 2019, pp. 1–12. [View] [8] J. M. Collins, J. Larrimore, and C. Urban, “Bank Accounts for Minors: A Pathway to Financial Inclusion or a Dead-End?” Review of Economics of the Household, vol. 22, no. 1, pp. 131–152, 2024. [View] [9] C. Zhenhe, “The Current Status and Influencing Factors of Minor’s Secondary Bank Account Usage in China,” International Journal of Frontiers in Sociology, vol. 6, no. 2, pp. 45–52, 2024. [View] [10] D. Cheng, Y. Zou, S. Xiang, and C. Jiang, “Graph Neural Networks for Financial Fraud Detection: A Review,” Frontiers of Computer Science, vol. 18, no. 4, p. 184001, 2024. [View]
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[11] B. Lebichot, W. Sibillini, G. M. Paldino, Y.-A. L. Borgne, F. Oblé, and G. Bontempi, “Assessment of Catastrophic Forgetting in Continual Credit Card Fraud Detection,” Expert Systems with Applications, vol. 240, p. 122448, 2024. [View] [12] A. Dal Pozzolo, G. Boracchi, O. Caelen, C. Alippi, and G. Bontempi, “Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy,” IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 8, pp. 3784–3797, 2018. [View] [13] M. Grover, P. Garg, and R. Agarwal, “Deterministic Verification versus Probabilistic Heuristics in Mission-Critical Core Banking Software,” IEEE Transactions on Reliability, vol. 72, no. 3, pp. 1012–1024, 2023. [View] [14] C. Autio, R. Schwartz, J. Dunietz, S. Jain, M. Stanley, E. Tabassi, P. Hall, and K. Roberts, “Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile,” National Institute of Standards and Technology, NIST Special Publication (AI 600-1), 2024. [View] [15] M. R. Bender et al., “A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents,” ACM Computing Surveys, vol. 56, no. 11, pp. 1–38, 2024. [View] [16] J. Černevičienė and A. Kabašinskas, “Explainable Artificial Intelligence (XAI) in Finance: A Systematic Literature Review,” Artificial Intelligence Review, vol. 57, no. 8, p. 216, 2024. [View] [17] M.-T. Wang, “Developing Digital Self-Regulation in Adolescence: A Developmental Determination Model of Digital Self-Regulation,” Educational Psychology Review, vol. 36, no. 2, pp. 95–118, 2024. [View] [18] A. Cecilia, M. F. Akbar, and S. Alfiansyah, “Family Communication and Financial Literacy to Prevent Children’s Consumptive Behavior in the Midst of Online Shopping and Paylater Trends,” Abdi Masyarakat Jurnal, vol. 4, no. 4, pp. 105–119, 2024. [View] [19] A. K. Chalise, “Engineering and Advancement of Digitization through Unified Payment Interface,” Journal of Engineering Research and Reports, vol. 26, no. 3, pp. 15–26, 2024. [View] [20] Federal Reserve Board, “Supervisory Guidance on Model Risk Management,” Supervision and Regulation Letters, SR-11-7, 2011 (Rev. 2024). [View] [21] S. R. Dubey et al., “Logical Neural Networks for Verifiable NeuroSymbolic AI in Critical Systems,” IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 5, pp. 2110–2124, 2024. [View] [22] M. Kleppmann, Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems. Sebastopol, CA, USA: O’Reilly Media, 2017. [View] [23] C. Westermeier, “From Flows Towards Updates: Security Regimes and Bordering Technologies for Financial Surveillance,” Review of International Studies, vol. 50, no. 4, pp. 625–644, 2024. [View] [24] T. J. Sathish, V. K. Naik, I. Mistry, and R. Mangrulkar, “Mitigating Prompt Injection Vulnerabilities in Enterprise Large Language Model Applications,” in Proc. IEEE Int. Conf. Computing, Communication and Security (ICCCS), 2023, pp. 1–6. [View] [25] I. Madaminov and O. Allamov, “A Hybrid Framework for ContextAware Auditing in Event-Driven Microservice Architectures,” International Journal of Science and Technology, vol. 2, no. 1, pp. 50–62, 2024. [View] [26] A. Kumar, R. Choudhary, S. Kumar, S. K. Kar, and R. Bansal, “The Growth Trajectory of UPI-Based Mobile Payments in India: Enablers and Inhibitors,” Indian Journal of Finance and Banking, vol. 16, no. 1, pp. 45–58, 2024. [View] [27] A. Soundalgekar, “National Financial Switch Simulator Using Mobile and Server Platforms,” International Journal for Research in Applied Science and Engineering Technology, vol. 12, no. 4, pp. 985–992, 2024. [View] [28] D. Díaz-Sánchez, A. Marín, F. Almenárez, P. Arias-Cabarcos, and R. S. Sherratt, “Security Challenges and Certificate Pinning Techniques for IoT and M2M Secure Communications,” IEEE Communications Surveys & Tutorials, vol. 26, no. 2, pp. 1120–1145, 2024. [View] [29] Arm Limited, “Arm TrustZone Technology for Armv8-A Architecture,” Technical White Paper, Arm Security Architectures, 2020. [View] [30] U. K. A. Sethupathy, “Evolving Frameworks in Digital Payment Security: Technological Innovations, Economic Impact, and Consumer Trust Dynamics,” International Journal of Computational and Experimental Science and Engineering, vol. 10, no. 3, pp. 1–12, 2024. [View] [31] Cloud Security Alliance, “Security Guidance for Critical Areas of Focus in Cloud Computing: Generative AI Governance,” CSA Research White Paper, 2023. [View] [32] OpenTelemetry Consortium, “Telemetry Data Governance and Automated Sensitive Attribute Scrubbing,” OpenTelemetry Community Specification, 2023. [View]
[33] L. J. George, S. A. Mubeen, M. O. Fareed, R. H. S. A. Sinbi, and D. George, “Leveraging Tokenization for Enhanced Security in Banking and Financial Services,” Journal of Research in Business and Management, vol. 12, no. 3, pp. 65–74, 2024. [View] [34] J. Lu, A. Liu, F. Dong, F. Gu, J. Gama, and G. Zhang, “Learning Under Concept Drift: A Review,” IEEE Transactions on Knowledge and Data Engineering, vol. 31, no. 12, pp. 2346–2363, 2019. [View] [35] K. Bauer, M. von Zahn, and O. Hinz, “Expl(AI)ned: The Impact of Explainable Artificial Intelligence on Users’ Information Processing,” Information Systems Research, vol. 35, no. 4, pp. 1580–1602, 2024. [View] [36] L. Huang et al., “A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions,” ACM Transactions on Information Systems, vol. 42, no. 2, pp. 1–40, 2024. [View] [37] C.-H. Su, F. Tu, X. Zhang, B. Shia, and T. Lee, “An Ensemble Machine Learning Based System for Merchant Credit Risk Detection in Merchant MCC Misuse,” Expert Systems with Applications, vol. 238, p. 122083, 2024. [View]