The increasing digitization of financial ecosystems has introduced both operational efficiency and heightened exposure to fraud, misreporting, and systemic financial risk. Remote ledger bookkeeping systems, while enabling distributed accounting operations, remain vulnerable to inconsistent transaction validation, delayed anomaly detection, and limited predictive capability for financial exposure forecasting. This paper proposes an AI-augmented neural architecture designed to enhance remote ledger bookkeeping through integrated fraud detection and exposure forecasting mechanisms. The proposed framework combines deep neural networks with blockchain-inspired integrity assurance mechanisms and cloud-based distributed processing to ensure both data reliability and predictive intelligence.
The architecture builds upon advancements in AI-driven financial systems and decentralized computing paradigms. Prior studies highlight the convergence of blockchain, IoT, and secure distributed frameworks for enhancing trust and transparency in smart systems (Kumari et al., 2021; Cha et al., 2021). Similarly, AI-enabled fraud detection models demonstrate significant improvements in identifying anomalous patterns in real time (Kodela et al., 2026). This research extends these foundations by integrating neural sequence modeling for ledger transaction analysis and exposure forecasting modules that predict financial risk trajectories based on temporal transaction behavior.
The proposed system introduces a multi-layered processing pipeline consisting of data ingestion, ledger normalization, anomaly detection via recurrent neural networks, fraud classification using hybrid feature embeddings, and exposure forecasting using temporal convolutional forecasting models. The architecture is further reinforced with distributed validation mechanisms inspired by blockchain-empowered secure architectures (Islam et al., 2022; Rahman et al., 2022).
Findings suggest that AI-augmented ledger systems significantly improve fraud detection accuracy while reducing latency in risk identification. Additionally, exposure forecasting models demonstrate improved predictive stability in volatile financial environments. The study contributes a scalable, secure, and intelligent framework for next-generation financial bookkeeping systems, with implications for fintech platforms, enterprise accounting systems, and decentralized financial infrastructures.