AI-Driven Neural Architecture for Internet-Based Ledger Management with Fraud Identification and Exposure Prediction

Abstract

The rapid evolution of distributed ledger technologies (DLTs) and internet-based financial ecosystems has significantly transformed the way transactional data is stored, validated, and analyzed. However, despite their inherent advantages in transparency and immutability, modern ledger systems remain vulnerable to sophisticated fraud patterns, adversarial manipulation, and hidden exposure risks. This research proposes an AI-driven neural architecture designed for internet-based ledger management with integrated fraud identification and exposure prediction capabilities.

The study synthesizes principles from blockchain-based trust systems, distributed ledger frameworks, and deep learning-based intrusion detection mechanisms to construct a hybrid intelligent model capable of real-time transaction monitoring and predictive risk evaluation. Prior studies highlight the increasing role of neural networks in cybersecurity applications (Drewek-Ossowicka et al., 2021), temporal graph-based cyberattack detection (Duan et al., 2024), and optimized recurrent architectures for intrusion detection (Kumar et al., 2024). Building upon these foundations, the proposed architecture integrates temporal graph modeling, collaborative feature mapping, and dimensionality reduction techniques to enhance detection accuracy and computational efficiency.

The system further aligns ledger governance with AI-enhanced fraud analytics inspired by distributed trust mechanisms and consensus-based ledger models (Bellini et al., 2020; Chowdhury, 2019). Additionally, the architecture incorporates predictive exposure analysis mechanisms that estimate financial risk propagation across interconnected ledger nodes, drawing conceptual parallels with real-time fraud prediction frameworks in cloud accounting systems (Kodela et al., 2026).

Experimental synthesis and comparative evaluation indicate that neural ledger architectures significantly improve anomaly detection rates while reducing false positives in dynamic transactional environments. The proposed model demonstrates scalability across distributed systems and adaptability to heterogeneous ledger infrastructures, including public and private blockchain networks.

The study contributes a unified framework that bridges AI-driven cybersecurity, distributed ledger management, and predictive financial analytics. It provides a structured pathway for deploying intelligent ledger systems capable of proactive fraud mitigation and exposure forecasting in complex digital economies.