Intelligent Neural Network Framework for Distributed Digital Ledgers with Live Scam Detection and Risk Forecasting

Abstract

The rapid expansion of distributed digital ledger technologies, particularly blockchain-based systems, has introduced new paradigms in decentralized finance, data integrity, and autonomous transaction systems. However, the increasing sophistication of cyber fraud, financial manipulation, and real-time scam strategies has exposed critical vulnerabilities in ledger ecosystems. This research proposes an intelligent neural network framework integrated with distributed digital ledger systems to enable live scam detection and predictive risk forecasting.

The study synthesizes advancements in machine learning, risk evaluation methodologies, and intelligent decision systems to construct a hybrid analytical architecture. The framework leverages deep neural networks for anomaly detection, probabilistic risk scoring, and adaptive learning mechanisms capable of evolving with transactional behaviors. Drawing from established methodologies such as fuzzy analytic hierarchy processes (Hu Wei et al., 2005), clustering-based expert weighting systems (Lihua He et al., 2014), and multi-factor risk evaluation models (Qiang Sun et al., 2012), the proposed system integrates structured decision intelligence with real-time computational learning.

Furthermore, reliability modeling techniques inspired by complex distribution systems (Weixing Li et al.) and cooperative game-based assessment frameworks (Yan Xu & Xin Chen, 2015) are incorporated to enhance system robustness. The study also builds upon recent advancements in deep learning-based financial fraud prediction systems (Kodela et al., 2026), which demonstrate the effectiveness of neural architectures in real-time risk environments.

The proposed framework introduces a dual-layer architecture: a blockchain integrity layer ensuring secure distributed consensus, and an intelligence layer responsible for continuous monitoring, anomaly detection, and predictive analytics. Experimental simulations indicate that neural-enhanced ledger systems significantly improve fraud detection accuracy, reduce false positives, and enable proactive risk mitigation.

The findings suggest that integrating deep learning with distributed ledger infrastructures offers a transformative approach to financial cybersecurity, enabling adaptive, scalable, and self-learning risk management systems suitable for next-generation digital economies.