Advanced Neural Network-Based Distributed Ledger System for Scam Detection and Monetary Exposure Forecasting

Abstract

The rapid expansion of digital financial ecosystems has significantly increased exposure to fraudulent activities, including sophisticated scams, transactional manipulation, and systemic monetary risks. Traditional rule-based fraud detection systems are increasingly insufficient in addressing the dynamic and adaptive nature of cyber-enabled financial threats. In response, this research proposes an advanced neural network-based distributed ledger system designed for scam detection and monetary exposure forecasting, integrating deep learning architectures with distributed ledger technology (DLT) to enhance transparency, immutability, and predictive intelligence in financial ecosystems.

The study synthesizes principles from blockchain and distributed ledger frameworks (Blockchain vs Database, n.d.; Distributed Ledger Technology, n.d.) with neural predictive modeling to construct a hybrid analytical system capable of real-time anomaly detection and forward-looking financial risk estimation. The proposed system leverages decentralized transaction validation, ensuring data integrity while simultaneously enabling machine learning models to operate on tamper-resistant datasets. This integration addresses critical limitations in centralized financial systems, particularly in relation to data manipulation risks and delayed fraud detection.

A key contribution of this research is the incorporation of deep learning-enhanced financial modeling techniques inspired by real-time fraud prediction frameworks (Kodela, S., Kurada, S. B., Mogili, V. B., & Duggirala, J., 2026), which demonstrate the applicability of neural architectures in identifying hidden transactional patterns and forecasting financial exposure. The proposed system extends these ideas into a distributed ledger environment, enabling continuous learning from verified transactional streams.

The research further contextualizes financial vulnerability through macro-level risk indicators drawn from global cybersecurity surveys and energy-economic transitions (EY Global Information Security Survey 2018–19, 2019; Ritchie & Roser, 2020), highlighting the increasing interconnectedness between digital infrastructure and financial risk landscapes. Additionally, the Industrial Internet of Things (IIC, 2015) provides a structural foundation for understanding scalable distributed environments in which financial transactions and data streams coexist.

The findings suggest that integrating neural networks with distributed ledger systems enhances fraud detection accuracy, reduces latency in anomaly identification, and improves predictive forecasting of monetary exposure. However, challenges remain in computational scalability, model interpretability, and cross-system interoperability.