Problem
Research questions and friction points this paper is trying to address.
AI supervision
network fraud
decision management
blockchain auditability
role-aware framework
When Does AI Supervision Help? A Role-Aware Study of Network Fraud Decision Management with Blockchain Auditability
🤖 AI Summary
This study addresses when AI supervision substantively enhances online fraud decision-making rather than merely imposing operational overhead. It proposes a role-aware Decider-Supervisor framework that integrates federated learning (FedAvg with QLoRA) and large language models, incorporating blockchain smart contracts to enable full-lifecycle auditing. The intervention effects and conditional verification calibration are systematically evaluated across diverse configurations. The findings reveal that the value of supervision depends on role allocation, calibration mechanisms, and traffic composition rather than the mere stacking of models. Empirical results demonstrate that supervision significantly reduces misjudgments only in extremely high-fraud scenarios, thereby providing critical theoretical foundations and practical guidance for the efficient deployment of AI supervision systems.
📝 Abstract
When does a second artificial intelligence (AI) component improve a primary network-fraud decision rather than add operational burden? We study this question through a role-aware Decider-Supervisor (DS) framework with blockchain auditability, evaluating four directional configurations that combine centralised machine learning, a Federated Averaging (FedAvg)-trained federated meta-model, and Base or Quantized Low-Rank Adaptation (QLoRA) large language model variants. The analysis compares primary-only and supervised decisions using non-hard fraud performance, intervention burden, conditional calibration, traffic-mix and Review-capacity sensitivity, dependability tests, and blockchain lifecycle controls. The deterministic hard gate resolves 89.994% of fraudulent requests, leaving the non-hard population as the main AI decision setting. Conditional validation calibration does not produce a consistently transferable supervisory advantage on deployment replay. DS-3 QLoRA is the least disruptive supervised configuration, but it still underperforms its primary FedAvg stage in F1 and total errors. Across 36 reweighted traffic mixtures, supervision reduces total errors only for DS-4 Base in two extreme high-fraud scenarios. Blockchain tests support digest verification, tamper detection, authorisation, single-use review resolution, and post-finalisation integrity, while exposing a pre-finalisation single-write limitation. The results show that the value of AI supervision depends on role assignment, calibration, escalation policy, traffic composition, and lifecycle controls rather than on the presence of a second model alone.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Decider-Supervisor framework
Federated Averaging
QLoRA
Blockchain auditability
Network fraud detection
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