IdenTransformer: A Foundation Model Architecture for Robust Digital Identity Verification - Suman Kumar Sanjeev Prasanna (International Journal of Intelligent Systems and Applications in Engineering, 2025)
This research introduces a novel architecture for digital identity verification by leveraging the representational capabilities of large-scale foundation models. Traditional discriminative approaches for fraud detection often struggle to generalize across heterogeneous identity signals and evolving adversarial behaviors. The proposed framework, IdenTransformer, employs a multimodal transformer architecture that integrates biometric embeddings, behavioral telemetry, and relational metadata through cross-attention mechanisms within a unified latent representation space. Central to the approach is a parameter-efficient fine-tuning strategy using Identity-Adapters, which enables effective adaptation of large pre-trained models to high-cardinality identity verification tasks without requiring full model retraining. The framework further incorporates contrastive representation learning to enforce identity consistency across modalities while preserving sensitivity to anomalous behavioral patterns indicative of synthetic identity creation and coordinated fraud activity. Empirical evaluation on large-scale identity transaction datasets demonstrates significant improvements in fraud detection performance, including a 15% increase in Area Under the Precision–Recall Curve (AUPRC) and a 12% reduction in False Discovery Rate compared with strong ensemble-based baselines. The results demonstrate that foundation-model architectures provide a scalable and robust approach for detecting emerging identity fraud patterns in complex digital ecosystems.