S. Lakshmi
Persuasive textual narratives, bogus visual evidence, disreputable update patterns and absent accountability systems are being increasingly used to deceive backers in fraudulent crowdfunding campaigns. Current fraud detection techniques are primarily based on static information, on text-only indicators, or on very shallow fusion of multimodal information, and they are not able to detect deceptive information that evolves over time or is inconsistent across different modalities. This study presents a Temporal Cross-Modal Trust Intelligence Framework to mitigate reward-based crowdfunding fraud that is explainable. The framework combines Hidden Method-of-Moments Markov modelling for latent temporal behaviour analysis, Polynomial Expansion Canonical Correlation Analysis for nonlinear text–image consistency evaluation and a Frequency-Gated GRU classifier to distinguish subtle drift in behaviour from sudden suspicious behaviour anomalies. Local Outlier Factor-based risk refinement is also added to detect rare and locally abnormal fraud patterns, and a blockchain-auditable layer ensures prediction outcomes are transparent and tamper-proof, enhancing the decision-making process. Experimental results on a multimodal crowdfunding dataset created from the Kickstarter platform show that the proposed model achieves better accuracy, recall, F1-score, ROC-AUC, PR-AUC, and calibration reliability than conventional multimodal, transformer-based, and recurrent neural network models and classical machine learning. The results validate the proposed solution, which is built on the four temporal dynamics, cross-modal consistency, anomaly refinement and auditability, to be a comprehensive and interpretable solution for detecting early crowdfunding fraud.