Freight Fraud as A Cross-Source Data Consistency Problem: A Human-In-The-Loop Framework for Risk-Aware Transportation Operations

Abstract

Background: Freight-fraud controls frequently emphasize the validation of identities, documents, and credentials, while operational evidence is distributed across carrier records, telematics, fuel activity, shipment events, and communications. Objective: This paper develops a conceptual framework for detecting potentially fraudulent freight activity through inconsistencies among independently generated operational signals. Methods: A practice-informed conceptual synthesis was conducted using an early-stage founder narrative as the problem-definition source and published scholarship on statistical fraud detection, anomaly detection, machine-learning system reliability, and responsible AI as theoretical anchors. No transaction-level dataset or model deployment was available for empirical evaluation.

Results: The synthesis produced a five-layer cross-source risk framework encompassing data acquisition and provenance, temporal and entity alignment, contextual consistency testing, risk prioritization, and human adjudication. Five falsifiable propositions and an evaluation protocol are specified for future testing. The framework differentiates evidence of inconsistency from proof of fraud and explicitly accounts for legitimate exceptions, missing information, latency, and sensor errors. Conclusion: Multi-signal consistency analysis may augment credential-based freight verification, but its effectiveness and fairness require prospective validation against documented outcomes. The contribution is a testable analytical architecture rather than a demonstrated fraud-detection product.